The Role of Trust in Collaborative Filtering Role of Trust in Collaborative Filtering Neal Lathia,...

The Role of Trust in Collaborative Filtering

Neal Lathia, Stephen Hailes, Licia Capra

Abstract Recommender systems are amongst the most prominent and successfulfruits of social computing; they harvest profiles from a community of users in or-der to offer individuals personalised recommendations. The notion of trust playsa central role in this process, since users are unlikely to interact with a system orrespond positively to recommendations that they do not trust. However, trust is amulti-faceted concept, and has been applied to both recommender system inter-faces (to explore the explainability of computed recommendations) and algorithms(to algorithmically reproduce the social activity of exchanging recommendations inan accurate and robust manner). This chapter focuses on the algorithmic aspect oftrust-based recommender systems. When recommender system algorithms manipu-late a set of ratings, they connect users to each other, either implicitly or by explicittrust relationships: users, in effect, become each others recommenders. This chaptertherefore describes the key characteristics of trust in a collaborative environment:subjectivity, or the ability to include asymmetric relationships between users in asystem, the adaptivity of methods for generating recommendations, an awareness ofthe temporal nature of the system, and the robustness of the system from maliciousattack. The chapter then reviews and assesses the extent to which current modelsexhibit or reproduce the properties of a network of trust links; we find that whileparticular aspects have been throroughly examined, a large proportion of recom-mender system research focuses on a limited faction of trust relationships.

1 Introduction

Recommender sytems have experienced a growing presence on the web, becomingevermore accurate and powerful tools to enhance users’ online experience [1]. The

Neal Lathia, Stephen Hailes, Licia CapraDepartment of Computer Science, University College London, London WC1E 6BT, UKe-mail: n.lathia, s.hailes, [email protected]

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2 Neal Lathia, Stephen Hailes, Licia Capra

motivation for their existence is two-fold: on the one hand, users need to be pro-vided with tools to confront the growing problem of web information overload; onthe other hand, users respond to and seek personalised, tailored items on the webthat will cater for their individual needs. Collaborative filtering provides an auto-mated means of building such systems, based on capturing the tastes of like-mindedusers, and has grown to become the dominant method of sifting through and recom-mending web and e-commerce content [1].

The traditional approach to collaborative filtering does not include any notion ofthe underlying system users, both in terms of who they are and how they implicitlyinteract with one another. For example, the k-Nearest Neighbour (kNN) algorithmhas been widely applied in this context [2, 3]. As described in [4], kNN can be bro-ken down into a number of steps. The first is neighbourhood formation: every user isassigned k neighbours, the k most similar other users in the system. The ratings fromthis neighbourhood are aggregated in the second step, to generate predicted ratingsfor each user. In the last step, the predictions are ordered to generate a list of rec-ommendations. This process is repeated iteratively, and recommender systems arecontinuously updated in order to include the latest ratings from each user. The samemethod can also be applied from the item-based perspective. In this case, rather thanpopulating user neighbourhoods with other users, each item in the system is given aset of similar items. The two approaches are exactly the same in terms of how theyoperate, but differ in the perspective that they take on the user-item ratings. In thischapter we adopt the language of the user-based approach (i.e. we discuss similaritybetween users rather than items); however, all of the algorithms reviewed can beequally applied using the item-based approach.

The kNN algorithm is not the only candidate available for collaborative filtering:the bulk of research on recommender systems has been focused on improving theaccuracy of collaborative filtering by exploring the many algorithms that can op-erate on a set of user-item ratings. As reviewed in [5], a range of classifiers havebeen applied to the problem; in particular, a number of classifiers are often com-bined to produce hybrid recommendation algorithms. The overriding factor is thatcollaborative filtering is a mechanism for implicitly connecting users to each otherbased on the opinions each member of the system has expressed. However, as weexplore below, there is a tight coupling between how well users understand the pro-cess that generated the recommendations they are given and the extent that they trustthem. Therefore, while accuracy remains an important metric of recommender sys-tem performance, the need to understand and explain the underlying and emergentmodels of user interaction in collaborative filtering gains greater traction. This re-sult has motivated a wide range of research that aims to incorporate the idea of trustbetween users into collaborative filtering. Trust needs to transpire from the users’experience with the system interface, to the algorithms that compute the recommen-dations and, ultimately, down to the honesty of the ratings used by the algorithmsthemselves.

The Role of Trust in Collaborative Filtering 3

The kNN algorithm has been subject to a number of modifications to improve therecommendations it generates; for example, [6] combines the algorithm with infor-mation about the content being recommended and [2] augments the user-similaritycomputation with a significance weight, to penalise user-pairs that do not share alarge number of common ratings. In fact, many of the techniques that we describebelow can be viewed in a similar light: they extend and modify traditional collabora-tive filtering in order to incorporate facets of human trust, albeit with different goalsin mind. This chapter therefore begins by exploring the underlying motivations forthe use of trust models and a discussion on trust itself, from a computational pointof view. The purpose of this chapter is to review the variety of approaches that havebeen adopted when modeling a recommender system as interactions between trust-ing users.

1.1 Notation

Prior to delving into the topic of trust in recommender systems, we define the no-tation that will be used throughout the chapter. The notation mainly relates to theratings that are input to collaborative filtering algorithms: a rating ru,i is the valuethat user u input for item i. This rating may be an explicit integer value, or inferredfrom the user’s behaviour. Nevertheless, it represents a judgement that user u hasexpressed on item i. The set of ratings Ru input by user u constitute user u’s profile,which has size |Ru|; equivalently, the set of ratings input for item i is denoted Ri,and has size |Ri|. Lastly, the mean rating r̄u of user u is the average of all ratingscontained in Ru.

2 Motivating Trust in Recommender Systems

The motivations for using a notion of trust in collaborative filtering can be decom-posed into three categories: (1) in order to accomodate for the explainability re-quired by system users, (2) to overcome the limitations and uncertainty that arisesfrom similarity-based collaborative filtering, and (3) to improve the robustness ofcollaborative filtering to malicious attacks. A consequence of incorporating trustmodels into collaborative filtering is also often a measurable benefit in terms ofprediction accuracy; however, state of the art algorithms that are only tuned for ac-curacy [3] do not mention trust models at all.

The first motivation is centred around the system users. For example, Tintarev [7],when expanding upon what an explainable recommendation consists of, cites user-system trust as an advantageous quality of a recommender system. Similarly, Her-locker et al [8] discuss the effect that explainability has on users’ perception of therecommendations that they receive, especially those recommendations that are sig-


nificantly irrelevant or disliked by the users. Chen and Pu [9] further investigate thisissue by building explanation interfaces that are linked to, and based on, a formalmodel of trust. Although a major component of these works revolve around pre-senting information to the end users, they recognise that building an explainablealgorithm is a key component of transparency: it converts a “black-box” recom-mendation engine into something that users can relate to. This is related to whatAbdul-Rahman and Hailes [10] describe as user-user context specific trust: users,who will be implicitly connected with one another by the collaborative filtering al-gorithm, need a mechanism to represent this interaction with each other during theprocess. Descriptions of trust models are based on the language of everyday humaninteraction, and therefore promise to fulfill this requirement.

The second motivation shifts focus from the users to the filtering algorithms them-selves. These algorithms rely on user-rating data to be input; however, ratings arenotoriously sparse. The volume of missing data has a two-fold implication. First,new users cannot be recommended items until the system has elicited preferencesfrom them [11]. Even when ratings are present, a user pair who may share commoninterests will never be cited in each other’s neighbourhood unless they share com-mon ratings: information cannot be propagated beyond each user’s neighbourhood.Secondly, computed similarity will be incomplete, uncertain, and potentially unreli-able. To highlight this point, Lathia et al [12] showed that assigning a random-valuedsimilarity between every pair of users in the MovieLens dataset produced compara-ble accuracy to baseline correlation methods. Why does this happen? Comparing thedistribution of similarity that emerges from the set of users when different metricsare used displays a strong disagreement between, say, the Pearson correlation coeffi-cient and the cosine-based similarity. Even the qualitative interpretation of similarity(i.e. positive values implying high similarity, negative values showing dissimilarity)is dependent on how similarity itself is measured and not on the ratings in user pro-files: a pair of profiles may shift from being highly similar to highly dissimilar whenone metric is replaced by another. These observations can themselves be interpretedin two ways; on the one hand, they highlight the inefficacy of accuracy-based eval-uations of recommender systems, and further evaluation metrics are required. Onthe other hand, they again emphasise the need to base these algorithms on methodsthat people can understand, in order to encourage participation and offer transparentreasons for recommendations. In other words, while collaborative filtering offers ameans of implicitly connecting people, it is not evaluated or treated as such.

The last motivation moves another level down, to the ratings themselves. Since rec-ommender systems are often deployed in an e-commerce environment, there aremany parties who may be interested in trying to game the system for their benefit,using what are known as shilling or profile injection attacks [13]. Items that are rec-ommended more will have greater visibility amongst the system’s customers; equiv-alently, items that are negatively rated may never be recommended at all, and theremay be measurable economic benefits from being able to control the recommen-dation process. The problem stems from the fact that collaborative filtering, when


automating the process of implicitly connecting users, operates in a near-anonymousenvironment. From the point of view of the ratings themselves, it is difficult to differ-entiate between what was input by honest users and the ratings that have been addedin order to perform an attack. This last motivation deals with the case where collab-orative filtering data itself may have been molded in order to influence the results.Trust models come to the rescue: by augmenting traditional collaborative filteringwith a notion of how users interact, the robustness of recommender systems can besecured.

3 Trust Models

The previous section highlighted an array of problems faced by collaborative filter-ing; what follows is a review of state of the art approaches that aim to address theseissues by extending collaborative filtering with the facets of human trust. However,before we proceed, we explore trust itself: what is trust? How has it been formalisedas a computational concept? Most importantly, what are its characteristics?

A wide range of research [10, 14, 15] begins from sociologist Gambetta’s defini-tion of trust [16]. Gambetta states “trust (or, symmetrically, distrust) is a particularlevel of the subjective probability with which an agent will perform a particular ac-tion [...].” Trust is described as the level of belief established between two entities ina given context. Discussing trust as a probability paved the way for computationalmodels of trust to be developed, as first explored by Marsh [17] and subsequentlyby a wide range of researchers [18]. The underlying assumption of trust models isthat users’ (or agents, peers, etc) historical behaviour is representative of how theywill act in the future: much like collaborative filtering, the common theme is oneof learning. The differences between the two emerges from the stance they adopttoward their target scenarios; unlike collaborative filtering, trust models are oftenadopted as a control mechanism (by, for example, rewarding good behaviour incommerce sites with reputation credit) and are user-centred techniques that are bothaware and responsive to the particular characteristics desired of the system (such as,in the previous example, reliable online trade).

Trust models have been applied to a wide range of contexts, ranging from onlinereputation systems (e.g. eBay.com) to dynamic networks [19] and mobile environ-ments [20]; a survey of trust in online service provision can be found in [14]. Due tothis, trust modeling and computational trust may draw strong criticism with regardsto their name: it is arguable that, in many of these contexts, “trust” is a vague syn-onym of “reliability,” “competence,” “predictability,” or “security.” However, encap-sulating these scenarios under the guise of trust emphasises the common themes thatflow between them; namely, that researchers are developing mechanisms for usersto operate in computational environments that mimic and reflect the way humansconduct these interactions between each other outside of the realm of information


technology.

Given this view of trust, what are the high-level common characteristics that emergefrom trust models? We outline four features of trust here:

1. Subjectivity: Trust relationships are, for the most part, asymmetric. In otherwords, if we represent a trust value of user a for user b as trust(a,b), then wemay have that trust(a,b) 6= trust(b,a).

2. Temporality: Trust model-based decisions are made by learning from previousinteractions. In other words, the set of historical interactions has a direct influenceon the current decisions; trust relationships are viewed as lasting through time.This particular point has lead to the application of game theory in the analysis oftrust, where interactions between users are viewed as a sequence of events [15].

3. Adaptivity: There is a hidden feedback loop built into trust models; current trustvalues affect the decisions that are made, which in turn affect the update of thetrust values. Given that trust relationships will be asymmetric, different users willlearn to trust at different rates (or potentially with different methods): trust-basedsystems therefore adapt according to the feedback provided by each user.

4. Robustness: Trust models are often built within a decision framework, the aimbeing to encourage interaction between user pairs that will lead to fruitful out-comes and discourage the participation of malicious users. Trust models aretherefore often deployed to secure contexts where traditional security paradigmsno longer hold.

In the following sections we look at how these trust models characteristics havebeen implemented in recommender systems; in particular, we examine the extentthat work done to date addresses the characteristics we have outlined above.

4 Using Trust For Neighbour Selection

One of the central roles that trust modeling has served in collaborative filtering isto address the problem of neighbour selection. Traditional approaches to collabo-rative filtering are based on populating users’ kNN neighbourhood with others whoshare the highest measurable amount of similarity with the them [2]. However, asdescribed above, these methods suffer may shortcomings, including:

• Poor explainability;• Vulnerability to attack;• Since they base their computations on profile intersections, it is likely that each

user share measurable similarity with a only small subset of other users;• Similarly, users who have recently joined the system and have next to no ratings

may have no neighbourhood at all; and• Similarity metrics are symmetric and are computed on the co-rated items between

user pairs, implying that high similarity is often shared with users who have very


sparse profiles if the few ratings they have input are the same as those co-ratedwith the current user.

These weaknesses dampen the ability for recommendations to be propagated arounda community of users. The aim of using trust for neighbour selection is to captureinformation that resides outside of each user’s local similarity neighbourhood in atransparent, robust and accurate way.

Two main approaches have been adopted: implicit methods, which aim to infer trustvalues between users based on item ratings, and explicit methods, that draw trustvalues from pre-established (or manually input) social links between users. Bothmethods share a common vision: the underlying relationships (whether inferred orpre-existing) can be described and reasoned upon in a web of trust, a graph whereusers are nodes and the links are weighted according to the extent that users trusteach other.

In this section, we review a range of techniques that have been applied and evaluatedfrom both of these perspectives. Comparing these methods side by side highlightsboth the common traits and differences that emerge from trust-based collaborativefiltering.

4.1 Computing Implicit Trust Values

The first perspective of trust in collaborative filtering considers values that can beinferred from the rating data. In other words, a web of trust between users is builtfrom how each user has rated the system’s content. In these cases, trust is used todenote predictability and to accomodate for the different ways that users interactwith the recommender system; in fact, many of these measures build upon genericerror measures, such as the mean absolute error.

For example, Pitsilis and Marshall focus on deriving trust by measuring the un-certainty that similarity computations include [21, 22]. To do so, they quantify theuncertainty u(a,b) between users a and b, which is computed as the average absolutedifference of the ratings in the intersection of the two user’s profiles. The authorsscale each difference by dividing it by the maximum possible rating, max(r):

u(a,b) =1

|Ra∩Rb| ∑i∈(Ra∩Rb)

(|ra,i− rb,i|

max(r)

)(1)

The authors then use this uncertainty measure in conjunction with the Pearson cor-relation coefficient to quantify how much a user should believe another. In otherwords, trust is used to scale similarity, rather than replace it. Similarly, O’Donovanand Smyth define a trust metric based on the recommendation error generated if asingle user were to predict the ratings of another [23, 24]. The authors first define a


rating’s correctness, as a binary function. A rating rb,i is correct relative to a targetuser’s rating ra,i if the absolute difference between the two falls below a thresholdε:

correct(ra,i,rb,i)⇐⇒ |ra,i− rb,i| ≤ ε (2)

The notion of correctness has two applications. The first is at the profile level,TrustP: the amount of trust that user a bestows on another user b is equivalentto the proportion of times that b generates correct recommendations. Formally, ifRecSet(b) represents the set of b’s ratings used to generate recommendations, andCorrectSet(b) as the number of those ratings that are correct, then profile-level trustis computed as:

TrustP(b) =|CorrectSet(b)||RecSet(b)|

(3)

The second application of Equation 2 is item-level trust TrustI ; this maps to the rep-utation a user carries as being a good predictor for item i, and is a finer-grained formof Equation 3, as discussed in [23]. Both applications rely on an appropriate valueof ε: setting it too low hinders the formation of trust, while setting it too high willgive the same amount of trust to neighbours who co-rate items with the current user,regardless of how the items are rated (since correct is a binary function). Similar toPitsilis and Marshall, this metric also operates on the intersection of user profiles,and does not consider what has not been rated when computing trust.

Lathia, Hailes and Capra approach trust inference from a similar perspective, butextend it from a binary to continuous scale and to include ratings that fall outsideof the profile intersection of a user pair [25]. To do so, rather than quantifying thecorrectness of a neighbour’s rating, they consider the value that b’s rating of item iwould have provided to a a’s prediction, based on a’s rating:

value(a,b, i) = 1−ρ|ra,i− rb,i| (4)

This equation returns 1 if the two ratings are the same, and 0 if user b has notrated item i; otherwise, its value depends on the penalising factor ρ ∈ [0,1]. Therole of the penalising factor is to moderate the extent to which large differencesbetween input ratings are punished; even though the two ratings may diverge, theyshare the common feature of having been input to the system, which is neverthelessrelevant in sparse environments such as collaborative filtering. A low penalisingfactor will therefore have the effect of populating neighbourhoods with profiles thatare very similar in terms of what was rated, whereas a high penalising factor placesthe emphasis on how items are rated. In [25], the authors use ρ = 1

5 . The trustbetween two users is computed as the average value b’s ratings provide to a:

trust(a,b) =1|Ra|

(∑

i∈Ra

value(a,b, i)

)(5)


This trust metric differs from that of O’Donovan and Smyth by being a pairwisemeasure, focusing on the value that user b gives to user a. Unlike the measures ex-plored above, the value sum is divided by the size of the target user’s profile, |Ra|,which is greater than or equal to the size of the pair’s profile intersection, |Ra∩Rb|,depending on whether a has rated more or less items than b. This affects the trustthat can be awarded to those who have the sparsest profiles: it becomes impossiblefor a user who rates a lot of content to highly trust those who do not, while not pre-venting the inverse from happening.

The three methods we have presented here are not the only proposals for trust infer-ence between users in collaborative filtering contexts. For example, Weng et al [26]liken the web of trust structure in a collaborative filtering context to a distributedpeer-to-peer network overlay and describe a model that updates trust accordingly.Hwang and Chen [27] proposed another model that again marries trust and simi-larity values, taking advantage of both trust propagation and local similarity neigh-bourhoods. Papagelis et al [28] do not differentiate between similarity and trust, bydefining the trust between a pair of users as the correlation their profiles share; theythen apply a propagation scheme in order to extend user neighbourhoods.

Many of the problems of computed trust values are akin to those of similarity; forexample, it is difficult to set a neighbourhood for a new user who has not ratedany items [11]. As the above work highlights, the characteristics of trust modelingallow for solutions that would not emerge from similarity-centric collaborative fil-tering. For example, Lathia et al extend the above measure to include a constantbootstrapping value β , which translates to initial recommendations that are basedon popularity, and would become more personalised as the user inputs ratings. How-ever, none of the measures take into account the potential noise in the user ratings,or the actual identity of the neighbours themselves (leading to system vulnerabilitiesthat are explored below).

All of the methods we have explored share the common theme of using error be-tween profiles as an indication of trust. Similarly, there is a broad literature onsimilarity estimation that does not adopt the language of trust modeling, such asthe “horting” approach by Aggarwal et al [29] and the probabilistic approach byBlanzieri and Ricci [30]. In all of the above, each user pair is evaluated indepen-dently; the significant differences appear in how each method reflects an underlyinguser model of trust.

4.2 Extracting Explicit Trust

The alternative to computing trust values between users is to transfer pre-existingsocial ties to the collaborative filtering context. There are two approaches that havebeen followed here: one the one hand, users may be asked to explicitly select trust-


worthy neighbours. On the other hand, social ties may be drawn from online socialnetworks by where it is possible to identify each user’s friends.

For example, Massa and Avesani describe trust-aware recommender systems [31,32]. In this scenario, users are asked to rate both items and other users. Doing sopaves the way to the construction of a web of trust between the system users. Sinceusers cannot rate a significant portion of the other users, the problem of sparsityremains. However, assuming that user-input trust ratings for other users are morereliable than computed values, trust propagation can be more effective. This chapterdoes not address the details of trust propagation; however, there are some pointsworth noting. Trust propagation is a highly explainable process: if a trust b, and btrust c, then it is likely that a will trust c. However, this transparency is obscuredas the propagation extends beyond a two-hop relationship. Propagation sits on theassumption that trust is transitive, an assumption that can be challenged once thepropagation extends beyond “reasonable” limits. In small-world scenarios (such associal networks), this limit is likely to be less than the famed six-degrees of sepa-ration, since it is apparent that people do not trust everyone else in an entire socialnetwork. Much like similarity and computed trust, the efficiency of trust propagationis therefore dependent on the method used and the characteristics of the underlyingdata.

A range of other works centre their focus on the social aspect of recommenda-tions. For example, Bonhard and Sasse [33, 34] perform a series of experimentsthat analyse users’ perception of recommendations: they conclude that participantsoverwhelmingly prefer recommendations from familiar recommenders. The exper-iments reflect the ongoing asymmetry between algorithmic approaches to collab-orative filtering, which tend to focus on predictive accuracy, and user studies thatmainly consider recommender system interfaces. It is difficult to evaluate one in-dependently of the other, and Bonhard’s motivations for the use of social networksecho those used to motivate the use of trust models in Section 2: in order to reconcilethe end users’ mental model of the system, and the system’s model of the users.

Golbeck explored the power of social networking in the FilmTrust system [35],showing that these systems produce comparable accuracy to similarity-based col-laborative filtering. Research along these lines departs from pure trust-based model-ing towards the Semantic Web [36] and multi-agent systems [37]. The application ofsocial networks can also be beneficial to collaborative filtering since relationships inthe web of trust can be augmented from simple weighted links to annotated, contex-tual relationships (i.e. b is my sister, c is my friend). Context-aware recommendersystems is a nascent research area; Amodavicius et al [38] provide a first view intothis subject by looking at multi dimensional rating models. Full coverage of thisfalls beyond the scope of this chapter; however, it is apparent how network ties canbe fed into mechanisms that include who and where the users are before providingrecommendations.


The main criticism of many of these approaches is that they require additional man-ual labour from the end user; in effect, they move against the fully automated viewof recommender systems that original collaborative filtering proposed. However, so-cial networks are on the rise, and users proactively dedicate a significant portion oftime to social networking. The implementation of these methods therefore aims toharness the information that users input in order to serve them better.

It is important to note that both the computed and explicit methods of finding trust-worthy neighbours are not in conflict; in fact, they can be implemented side by side.Both require users to be rating items in order to provide recommendations, while thelatter also requires social structure. Popular social networking sites, such as Face-book1 include a plethora of applications where users are requested to rate items,making the conjunction of the two methods evermore easy.

4.3 Trust-Based Collaborative Filtering

Once neighbours have been chosen, content can be filtered. However, there are arange of choices available to do so; in this section we outline the methods imple-mented by the researchers we discussed in the previous section. The approachesrevolve around rating aggregations, in other words, taking a set of neighbour ratingsfor an item and predicting the user’s rating. The most widely adopted is Resnick’sformula [39], where the predicted rating r̂a,i of item i for user a is computed basedon the weighted ratings from each neighbour:

r̂a,i = r̄a +∑(rb,i− r̄b)×wa,b

∑wa,b(6)

The difference between each method is (a) what neighbours are selected, and (b)how the ratings from each neighbour are weighted. We decompose the methods intothree strategies, trust-based filtering, weighting, and social filtering.

1. Trust-Based Filtering. In this case, neighbours are selected (filtered) using thecomputed trust values. The ratings they contribute are then weighted accordingto how similar they are with the target user.

2. Trust-Based Weighting departs fully from similarity-based collaborative filter-ing: neighbours are both selected and their contributions weighted according tothe trust they share with the target user.

3. Social Filtering. Neighbours are selected based on the social ties they share withthe target user. Ratings can then be weighted according to either their sharedsimilarity or trust with the target user.

All of these methods assume that users will be using the ratings scales symmetri-cally, i.e. if two users predict each other perfectly, then the difference (ra,i− r̄a) will

1 http://www.facebook.com/apps/


be the same as (rb,i− r̄b), regardless of what each user’s mean rating actually is. Inpractice, this is not always the case: predictions often need to be changed to fit ratingscale, since users each use this scale differently. This notion was first explored in theaforementioned work by Aggarwal et al [29], who aimed to find a linear mappingbetween different users’ ratings. However, Lathia et al [25] extend this notion to en-compass what they refer to as semantic distance, by learning a non-linear mappingbetween user profiles based on the rating contingency table between the two pro-files. The results offer accuracy benefits in the MovieLens dataset, but do not holdin all cases: translating from one rating scheme to another is thus another researcharea that has yet to be fully explored.

The above work further assumes that the underlying classifier is a kNN algorithm.Recent work, however, has been moving away from kNN-based recommender sys-tems. In fact, the data derived from users telling the system whom they trust canbe also input to other algorithms, such as matrix factorisation techniques [40, 41].In these works, Ma et al describe matrix factorisation models that account for bothwhat users rate (their preferences) and who they explicitly connect to (who theytrust). While certainly beneficial to cold-start users, introducing trust data into fac-torisation models reignites the problem of transparency: how will users understandhow their input trust values contribute to their recommendations? A potential av-enue for research lies in the effect of combining trust models on users. For example,Koren describes how a neighbourhood and factorisation model can be combined[42], and this work may begin to bridge the chasm between the model-based andtraditional kNN-based use of trust in recommender systems. One the other hand,recent work shows that the response of different users to the same algorithm maythemselves vary. In [43], the authors describe a number of experiments the show thepotential of selecting between rather than combining different recommender systemalgorithms; this relates to the broader notion of user-adaptivity that we describedabove.

The above trust-based methods have, for the most part, been evaluated accordingto traditional recommender system metrics: mean error, coverage and recommenda-tion list precision [44]. Mean error and coverage go hand in hand: the error considersthe difference between the predictions the system generates and the ratings input byusers and the coverage gives insight into the proportion of user-item pairs the sys-tem could make predictions for. There are a number of modified mean error metricsthat aim to introduce fairer representation of the users. For example, Massa uses themean absolute user error in order to compensate for the fact that more predictionsare often made for some users rather than others [32]; O’Donovan and Smyth com-pare algorithm performance by looking at how often one is more accurate than an-other [23], and Lathia et al measure mean error exclusively on items that have beenpredicted, to measure accuracy and coverage separately [25]. However, while thesehave provided explicit metrics that researchers have aimed to optimise towards, ac-curacy has disputed utility from the perspective of end-users’ [45]. Examining analgorithm from the point of view of top-N recommendations provides an alternative


evaluation; rather than considering the predictions themselves, information retrievalmetrics are used on a list of items sorted using the predictions. However, sorting alist of recommendations often relies on more than predicted ratings: for example,how should one sort two items that both have the highest possible rating predicted?How large should the list size N be? These kind of design decisions affect the itemsthat appear in top-N lists, and has motivated some to change from deterministicrecommendation lists to looking at whether items are “recommendable” (i.e. theirprediction falls over a predefined threshold) or not [46].

4.4 Are Current Methods Sufficient?

Given the above review of trust in collaborative filtering, both in terms of neigh-bourhood selection and neighbour rating aggregation, we return to the characteris-tics outlined in Section 3. In particular, we ask: do these algorithms address all thefeatures required of trust models?

1. Subjectivity. This characteristic stated that trust relationships are, for the mostpart, asymmetric, and is addressed by majority of the models presented above.The parallel that computed trust values have with error measures (or, their focuson the correctness of a rating with respect to another) ensures that the weight arelationship in the web of trust is given depends on which user is being consid-ered.

2. Temporality: This factor was based on the idea that trust model-based decisionsare made by learning from previous interactions. However, the perspective thatthe above models adopt is that all previous interactions can be captured in the rat-ing data; in fact, they do not consider whether users had been neighbours in thepast and any previous trust values shared between them. Recommender systemsare continuously updated, but none of the previous decisions that they made arefed forward toward making the best decision at the next update. Consider, for ex-ample, the manually input trust values described in Massa’s trust-aware system:these are not subject to aging or updated according to how useful they are. Thevalues remain static and responsibility for updating them is left to the user.

3. Adaptivity: The idea of adaptivity is based on the assumption that the “one sizefits all” model does not hold in recommender systems; the way users think aboutand rate items and respond to recommendations will vary between users. How-ever, all of the models presented above assume that the same model is applicableto all users: the one size fits all vision returns.

4. Robustness: The last quality related to the ability that users have to game thesystem’s recommendations. This topic is covered more extensively in Section 6;however, as explored by O’Donovan and Smyth [47], implementations of trustmay fend off certain shilling attacks, but paves the way for others to be adopted.

Based on the definition in Section 3, we find that much of the state of the art ad-dresses only one of the four facets of trust models, subjectivity. In the following


sections, we review further work from related fields that may respond to this voidand complete the trust-based vision of collaborative filtering.

5 Temporal Analysis of a Web of Trust

As seen above, trust models have primarily focused on creating the web of trust be-tween users; we can equivalently say that the role of trust has been to build a graphthat connects the system users (or items) to each other. Thinking of a recommendersystem in this way has lead to a growing body of work that analyses and makes useof the graph when recommending items. For example, Zanin et al [48] applied thesame analytic techniques used in complex network analysis to collaborative filter-ing graphs. Celma and Cano [49] used the same graph-based structure to highlightthe bias that popular artists introduce in music recommendation and discovery, andproposed a method to navigate users towards niche content based on graph traver-sal. Similar work by Zhou et al operates on citation graphs, and explores supervisedtechniques to exploit the graph structure [50]. Mirza et al [51] examine the graphs ofmovie rating datasets in order to evaluate the ability different collaborative filteringalgorithms have to connect users. All of these proposals begin by drawing the com-parison between recommender system graphs and real-world networks; moreover,they assume that the computed graph structure is a representative depiction of thelinks between content and then make their proposals based on this.

However, one of the aspects of recommender system graph analysis that is not con-sidered is the importance of time. Time can play three rather different roles in arecommender system; the most notable recent application of temporal features isusing when people rate items in order to better predict how they rate will them [52].A temporal view of recommender system also beckons the idea of recommendersystems devoted to developing, or incrementally changing, people’s tastes. Thesesystems could reflect and respond to growing knowledge of its users in the domainof items being recommended; an apparent application is in recommender systemsfor education. The last view of time relates back to recommender system graphs:as time passes and people continue rating items, the recommender system will beupdated and (in the case of computed trust relationships) the web of trust will berecreated. In the following sections we review work that analyses how these graphsevolve over time; in particular, we compare and highlight the difference betweenhow the two types of graphs evolve.

5.1 Explicit Trust Networks

The structure and evolution of explicit social networks has been extensively anal-ysed; for example, Kumar et al [53] looked at how two large Yahoo! social network


datasets changed over time. They identify three components in the graph: single-tons, who join the system and never connect to other users, the giant componentof connected users, and a middle region of isolated communities. In particular, theynote that majority of the relationships are reciprocal, and a significant fraction of thecommunity is found in the middle region. Interestingly, many of the isolated com-munities in the middle region formed star shaped graphs; they are formed of a singleuser who connects to a range of singletons (who may then only connect back to thiscentral user). The networks display an initial rapid growth followed by a period ofsteep decline and then settling on a pattern of steady growth; the authors postulatethat this comes as a consequence of both early adopters and the following major-ity of users joining the system. Collaborative filtering systems that rely on explicittrust are likely to demonstrate similar patterns, especially if they are purely basedon users’ social network. This structure highlights the weakness of explicit trust:it is difficult to propagate information across a community of users if the graph islargely disconnected and a large proportion of users are singletons or fall in isolatedcommunities.

5.2 Computed Web of Trust

Lathia et al [54] performed a similar analysis on rating data when the links betweenusers are computed and iteratively updated as the rating dataset grows. The maindifferences between this analysis and that of Kumar et al emerge from the natureof the kNN algorithm: each user’s out-degree will be bound by the k parameter(hence the out-degree distribution is constant), and users are not guaranteed to linkto others to whom they previously linked (i.e. links are not persistent). The authorsdecompose their analysis into a number of groups, considering single nodes, nodepairs, node neighbourhoods, and finally the entire graph, comparing results acrossa range of similarity measures. The difference between computed and explicit trustare highlighted if their results are compared to that of Kumar et al. Each analysisdisplay different growth patterns; in particular, the computed graphs do not exhibitthe same early adopter-late majority behaviour. The link weight (i.e. computed trustor similarity measure, depending on the algorithm details) between pairs of users isnot constant. In fact, the way that the weights evolve over time leads to a classifica-tion of similarity measures; some are incremental, displaying small variation overtime, others are corrective, initially awarding high similarity to neighbours and thenincrementally reducing the shared value, and the last set are near-random as there isno pattern governing the subsequent similarity shared between a pair of users, giventheir current value. This leads to an ever-changing ranking of neighbours, and eachuser’s neighbourhood is therefore also continuously subject to change. The equiva-lent of this result in Kumar et al’s work would have been observing very quick churnin isolated communities, which they do not report on. The entire computed graphalso differs from the explicit social network: once the k parameter is greater thanone, the graph is fully connected. Intuitively, as k is increased the graph will tend


toward becoming a clique; the only factors impeding each user from being linkedto everyone else are the k parameter and the requirement that user pairs co-ratea number of items to have a non-zero trust (and similarity) value. Unlike explicitsocial networks, the computed graph has a very short average path, small diame-ter, and much smaller level of reciprocity: collaborative filtering based on implicitconnectivity between users therefore generates a structure that seems to favour rec-ommendation propagation more than social networks. However, Lathia et al alsoexamine the in-degree distribution of users. Assuming that a node points to anotherif the latter is in the first’s top-k, then the in-degree distribution shows the “fairness”that collaborative filtering exhibits when assigning users to each other’s neighbour-hood. The authors found that this distribution has a very long tail. There is a distintbias in collaborative filtering: some users are highly cited in others’ neighbourhood,while others are never selected for this role. These so-called power users, whosein-degree falls at the head of the distribution, will exert a high amount of influeceon other’s recommendations. In the case of the datasets explored in [54], this subsetof users contribute positively to the system’s accuracy and coverage; however, thisleads to a strong vulnerability of collaborative filtering, as will be explored in thenext section.

5.3 Temporal Collaborative Filtering

The above works focus on analysing the temporal structure of the graphs that rec-ommendation algorithms can operate upon. The flipside of this work is to explorethe temporal performance of the same algorithms. The temporal performance ofcomputed trust is considered in [55]; rather than performing the traditional data par-tinioning into training and test sets, the authors view collaborative filtering data asa growing set that is continuously required to predict future ratings. In other words,given the current dataset, how well does the algorithm predict next week’s ratings?Then, after the subsequent week’s ratings have been added to the dataset, how welldoes the dataset predict the following week? The results that the authors report againhighlight the difficulty of accuracy-based evaluations: the relative performance ofdifferent collaborative filtering algorithms changes based on the current state of thedataset. In particular, different neighbourhood sizes (k values) offer varying levelsof accuracy to each of the system’s users. This observation leads to the proposal of asystem where the k values is adapted on a per-user basis, by modifying it accordingto the temporal performance measured to date. There is a need for similar work tobe done on the temporal performance of explicit/social network based recommenda-tions; in particular, it would be interesting to observe and measure the performanceof a system as the recommendation algorithm is itself changed, to be able to quan-tify the short and long term benefits of changing filtering algorithms.

Once collaborative filtering is cast onto the temporal scale, the evaluation metricsthat are used to measure performance need to be modified as well. While changes in


the structure of the graph can be measured in terms of neighbourhood convergence,accuracy is measured by transforming mean error metrics to include time. For ex-ample, the root mean squared error (RMSE) can be converted to the time-averagedRMSE:

RMSEt =

√∑

Nr̂u,i∈Rt

(r̂u,i− ru,i)2

|Rt |(7)

In other words, the RMSE achieved at time t is the root mean error on all predictionsmade to date. Similar transformations can be used to adapt coverage and recommen-dation list precision in order to evaluate the temporal performance of collaborativefiltering on dynamic data.

6 Building Robust Recommender Systems With Trust

As introduced in Section 1, the metaphor of trust needs to transpire from the userexperience with the system to the algorithm modeling interaction between users anddown to the ratings themselves. Trust in the rating data is motivated by the uncer-tainty regarding the intentions behind why users rate items (or other users) the waythey do. In an ideal world, each user would be expressing an independent, personalopinion of each item. However, it is more likely that other interests and factors per-vade the rating process [44]; furthermore, as explored above, the structure of theweb of trust implies that different users exert varying levels of influence [56] onothers, and some users may be interested in gaining as much influence as possible.

Research on attacks in collaborative filtering must make two apriori decisions. First,what constitutes an attack? This involves suitably formalising attack models in col-laborative filtering. Researchers often assume that attacks are executed by a set ofuser profiles; a single profile with malicious ratings is not considered. More impor-tantly, however, is the second: how is the success of an attack measured? Answer-ing this question requires appropriate metrics that reflect whether attackers haveachieved the purpose they intended. In this section, we briefly introduce attack mod-els and evaluation metrics in order to discuss the contributions that trust research hasmade to the area.

6.1 Attack Models

The first aspect of collaborative filtering-vulnerability research models the attacksthemselves. Examples of these attacks, as mentioned by Lam and Riedl [57], include


buying reputation credit on eBay and companies using fake ratings to promote recentproduct releases. Attacks are often viewed as pertaining to one of three groups [58]:

• Push Attack: Where the aim is to promote an item until it is recommended whereit otherwise would not be;

• Nuke Attack: Which aims to demote an item so that it is never recommended;• System Attack: Where the aim is to reduce the entire system’s performance in

order to attack the trust users have for the recommendations it produces.

At a high level, all attacks are performed by inserting a number of user profiles intothe system; each profile rates some items following a pre-defined strategy. They alsorate the target item in accordance with the attacker’s intentions (i.e. high ratings fora push attack, low ratings for a nuke attack). A variety of strategies have been im-plemented to rate the non-target items [59]; these include random attacks, where asubset of the items are rated around the overall mean rating, average attacks, where-upon items are rated according to the individual items’ mean, or bandwagon attacks,where subsets of items are rated randomly and popular items are rated highly. Finergrained differences between each attack can be drawn from the amount of knowl-edge used by attacks to achieve their goals. For example, attackers may or may nothave access to summary statistics about the items (including their mean rating andrating standard deviation); this differentiates between a high and low-knowledgeattack. Mobasher et al provide a formal definition of collaborative filtering attacks[13]. An attack model is a 4-tuple that consists of:

• A Choice Function. Given a target item i, the set of all items I and users U , thechoice function partitions the set I into three subsets: the set of selected items IS,the set of filler items IF , and the remaining set of unrated items I/0.

• A Filler and Selected Item Mapping Functions that will be used to generateratings for the items in IS and IF . These may be the same function (in the case ofrandom attacks), or may be different in order to modify the similarity betweenitems. As described in [13], a segment attack does the latter, and can be envisagedin scenarios where attackers are interested in binding the similarity between thetarget item and other content (for example, making a fantasy book highly similarto Harry Potter).

• A Target Rating Function that rates item i according to the attacker’s intention.

The process of performing an attack involves inserting these generated profiles intothe system; once they are there, the standard collaborative filtering algorithm (that isnot aware of malicious data) will automatically skew the recommendations for thetarget items. The difficulty of counteracting these attacks stems from the fact that itis difficult to differentiate between honest ratings (which perhaps express seeminglycontractory opinions) and the ratings of malicious profiles [60]. It is also difficultto differentiate between the instances where attacks have taken place and wherehighly inaccurate recommendations have been made. Therefore, a primary concernof research in this field has been on how to quantify the effect of an attack.


6.2 Measuring Attacks: Success or Failure?

A key component of a robust system is the ability to detect when an attack is takingplace. Similarly, researchers require methods to be able to measure both the vulner-ability that algorithms have to attack and the success that different attack strategieshave over collaborative filtering algorithms. While traditional collaborative filter-ing evaluation methods tend to focus on mean error, or the distance between theratings that users input and the predictions produced by the system, attack modelsare evaluated according to the change in performance produced by an attack, or thedifference between predicted ratings when attack profiles are present or not (theytherefore do not focus on the “true” rating input by the user [60]). O’Mahoney [58]defines two suitable metrics: a system’s robustness, which compares system perfor-mance pre- and post-profile injection, and stability, which focuses on the skew inthe target items’ predictions induced by the fake profiles.

Although the attack models described above aim to change the prediction for anitem, the attack is unlikely to have very much effect (from the users’ perspective)if the produced recommendations do not change; similarly, an attack is less effec-tive if it changes the position of a low-ranking item by a small amount. In otherwords, changing the top 10 recommendations users see is more likely to influencethem than changing the 90th - 100th recommendations. It therefore also becomesimportant to measure the likelihood that an item appear in a user’s top-N recom-mendations, which motivates the use of the hit ratio. The hit ratio is based on aproportion of binary values: an item’s score is incremented by 1 for each time itappears in a user’s recommendation list. However, the problem with the hit ratiometric is that it relies on a notion of top-N list, as explored above. It therefore alsoinherits the same difficulties that top-N evaluation suffers from.

Chirita et al [61] propose a number of metrics that can compliment algorithmsin order to identify when an attack is taking place. These include: the number ofpredictions that a single user profile is involved in, the user standard deviation, de-gree of agreement with others, and degree of similarity with top-k neighbours. Infact, they highlight how automated attacks will display consistent patterns, and canbe identified accordingly (although some non-shill profiles will also be identified asfalse-positives).

6.3 Robustness of Trust-Based Collaborative Filtering

Based on these metrics, we explore the extent that trust-based collaborative filteringmanages to produce attack-resistant recommendations. Lam and Riedl [57] showthat collaborative filtering algorithms differ in their ability to fend off attacks; inparticular, they show that the most effective attack push attack on an item-basedkNN algorithm produces the same results as the least effective attack on user-basedkNN. They discuss the results of a number of experiments using the MovieLens


dataset: they find it easier to push (rather than nuke) items in user-based kNN andthat new items are the most vulnerable to manipulation.

The intersection between trust modeling and attack resistance is related: both haveattempted to improve the way collaborative filtering selects neighbours for users.Mehta and Hofmann review the robustness of a variety of non-kNN collaborativefiltering methods [59]; in this section we focus on the robustness of the above trustmodels to attack. O’Donovan and Smyth discuss the reinforcement problem as aprimary vulnerability of their model (described in Section 4.1): if a large numberof injected profiles generate “correct” (Eq. 2) for each other, they will reinforce thetrust values endowed on them. The model proposed by Lathia et al [25], which isbased on pairwise computations, does not have this ripple effect. However, any trustcomputation that is based on measuring error between profiles is intuitively subjectto attack, and are an insufficient defense mechanism if implemented alone.

The explicit trust-based methods, described in Section 4.2, do not suffer from thesesame vulnerabilities. In fact, injected profiles would have little effect in such a sys-tem unless the profiles managed to befriend (or be trusted) by a number of users.On the one hand, this lessens the vulnerability of collaborative filtering; on theother hand, it does so by excluding all honest raters who are not directly trusted.Dell’Amico and Capra proposed a scheme that blends both explicit and implicitmethods [62]. To do so, they formalise relationships in a web of trust as being com-posed of two distinct, but equally important, characteristics: intent, or the willing-ness to provide honest ratings, and competence, the ability to provide correct ratings.An injected profile may therefore seemingly satisfy the competence requirement (bybeing appropriately populated with filler ratings) but will be excluded if its intentcannot be determined. Intent is measured by traversing the web of trust, taking ad-vantage of the structure of social networks and role that injected profiles play inthem [63].

A potential solution to this problem, approached from the perspective of gener-ating recommendations using identifiable raters, was explored by Amatriain et al[46]. In contexts where expert ratings are widely available (such as movies, sincea wide range of media publishes reviews that include ratings), the effect of profileinjection attacks can be nullified by only using the expert opinions when computingusers’ recommendations. This translates the collaborative filtering process from oneof identifying the best k neighbours for each to user (within a dataset or user com-munity) to matching each user with the best k experts (across different datasets).The solution is therefore centred around finding experts who each user trusts. Thework in [46] evaluates the potential of using a dataset where member’s identity isknown (since their ratings are crawled from their reviews in published media) topredict and produce useful recommendations for a much larger set of anonymoususers; the work highlights that while the experts are not as accurate as traditionalnearest neighbour filtering, they nevertheless provide recommendations that scorehighly in terms of recommendation list precision and are favoured by a large sample


of people questioned in a user study.

All of the above work views collaborative filtering attacks from the same perspectiveadopted in traditional evaluations, i.e. attackers will insert profiles, gain influence,and modify predictions in a single step. However, it is unclear both whether theabove models are sufficient and whether it may not be easier for attackers to adoptdifferent strategies. For example, large groups of users often proactively rate itemsthey have strong feelings against, such as political figures in an ongoing election2.The effect of groups of users rallying to nuke an item has the same effect as a profileinjection attack, except that it has not been initiated by fake profiles: removing theirratings from the system is likely to delete a large volume of valid information inorder to revert the effects of manually nuking a controversial item, and traditionalmethods seem no longer appropriate.

7 Conclusions

This chapter began by describing the three-fold motivation for the use of trust mod-els in collaborative filtering: trust models help to transform a “black-box” into atransparent, explainable recommender system; they also help users select neigh-bours in an intelligent way and access others who are not in their local neighbour-hood, and can address the vulnerability collaborative filtering has to malicious at-tack. Reviewing the trust model literature highlights the range of contexts wheretrust is deployed, and the similarities that arise between them. Trust models areused to construct, maintain, and moderate interaction between a set of indepen-dently operating users, agents, or peers. It is therefore possible to summarise thecharacteristics of trust models; in Section 3 we described subjectivity, temporality,adaptivity, and robustness.

The remainder of the chapter examined the extent that the use of trust in collab-orative filtering matches the requirements of a complete trust model. Majority of thework performed to date seeks to improve neighbour selection by reasoning on trust,which can be either computed, derived from the rating data, or explicit, by beingdrawn from users’ social networks. Both techniques can make use of trust propaga-tion to extend the reach of users’ neighbourhoods, and can be implemented side byside to draw the benefits of each method. Trust modeling in collaborative filteringhas tendentially centred its focus on the kNN approach, however, recent contribu-tions are beginning to explore how to reason with trust-value datasets with otherclassifiers.

As discussed in Section 4.3, trust-based neighbourhood selection only addressesone of the four identified features of trust (subjectivity, adaptivity, temporality, ro-

2 Robust Recommendation. Robin Burke, ACM RecSys ’08 Tutorial


bustness); it does not examine the temporal aspect of trust relationships, has beenimplemented as a non-adaptive “one size fits all” trust model, and remains vulner-able to attacks. To seek for insight into these characteristics, the chapter lookedat work in social network analysis, comparing the differences between explicit so-cial network and computed web of trust evolution in Section 5, and discussing theopportunity that temporal changes offer to apply adaptive user-centric techniquesto collaborative filtering. Lastly, Section 6 formalised the set of attacks that havebeen studied in the literature and reviewed the extent that trust models address theseweaknesses.

Trust has also been applied to recommender systems that operate in a distributedsetting [64]. In fact, as recommender systems are ported from centralised web en-vironments to distributed and mobile contexts, they inherit and intensify the pre-existing problems faced by collaborative filtering by adding the uncertainty of boththe provenance and trustworthiness of ratings as they are exchanged between peers.This context also provides a useful framework for extending the notion of trust. Inthis chapter, we only considered “positive” trust: users who explicitly tell the systemthat they trust another, or computed trust values that determined who the most trust-worthy neighbours were. There is certainly space for extending this in the future, toinclude, for example, distrust [65].

The striking aspect of trust model research related to collaborative filtering is theindependent goals set by each method: trust models have been implemented andevaluated to address a single characteristic of trust, while the characteristics seemintuitively correlated. For example, building a robust algorithm assumes that usersneighbourhoods should not be veered toward injected, noisy profiles and should beable to quickly adapt to changing the changing data environment when an attack ismounted: research into the utility of trust model application includes a broad set ofunanswered questions that beg for attention.

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