ABSTRACTRetrieval systems have greatly improved over the last half century,estimating relevance to a latent user need in a wide variety of areas.One common task in e-commerce and science that has not enjoyedsuch advancements is searching through a catalog of items. Findinga desirable item in such a catalog requires that the user specifydesirable item properties, specifically desirable attribute values.Existing item retrieval systems assume the user can formulate agood Boolean or SQL-style query to retrieve items, as one woulddo with a database, but this is often challenging, particularly givenmultiple numeric attributes. Such systems avoid inferring queryintent, instead requiring the user to precisely specify what matchesthe query. A contrasting approach would be to estimate how wellitems match the user’s latent desires and return items ranked bythis estimation. Towards this end, we present a retrieval modelinspired by multi-criteria decision making theory, concentratingon numeric attributes. In two user studies (choosing airline ticketsand meal plans) using Amazon Mechanical Turk, we evaluate ournovel approach against the de facto standard of Boolean retrievaland several models proposed in the literature. We use a novel com-petitive game to motivate test subjects and compare methods basedon the results of the subjects’ initial query and their success in thegame. In our experiments, our new method significantly outper-formed the others, whereas the Boolean approaches had the worstperformance.
CCS CONCEPTS• Information systems → Retrieval models and ranking; Similar-ity measures; Learning to rank;
ACM Reference Format:Shawn R. Wolfe and Yi Zhang. 2018. Item Retrieval as Utility Estimation.In SIGIR ’18: The 41st International ACM SIGIR Conference on Research &Development in Information Retrieval, July 8–12, 2018, Ann Arbor, MI, USA.ACM,NewYork, NY, USA, 10 pages. https://doi.org/10.1145/3209978.3210053
1 INTRODUCTIONSearching for items by their attribute values or metadata is a com-monplace task today. For example, consider searching for a partic-ular research paper you recall as published in 2013, or computermonitors under $200. Current search tools support a retrieval stylemore akin to a database than a modern information retrieval system,most often with faceted or Boolean search. In that model, usersplace hard constraints on acceptable attribute values to limit theresult set.
Unfortunately, this rigid style of retrieval often creates difficul-ties for the user. In the examples above, what if the sought researchpaper was published in 2012, not 2013 as remembered? There arethousands of computer monitors under $200, far too many to exam-ine, but constraining all desired attributes might yield no results.And what if a monitor that best fits the user’s desire is just outsidethe stated range, listing at $213? Boolean retrieval often yields noresults or too many. Faceted search usually avoids empty resultsets, but the facets are often pre-computed and may not match theuser’s intent well.
The problem with such retrieval systems is that they do not tryto understand what the user is seeking. Instead, they give the user atool to explicitly manage the result set by stating what to return andhow to order it. In this paper, we explore an alternative approachthat is more aligned with current information retrieval approaches –implicitly managing result sets by estimating relevance to a descrip-tion of the sought item. We model the user’s utility function, and inthe process, allow the system to trade off among conflicting criteriaon the user’s behalf, and in this way get closer to the underlyingquery intent. In contrast to the Boolean and faceted approaches inuse today, our approach does not use constraints.
We evaluate this new approach against the de facto standard ofBoolean retrieval and several models proposed in the literature intwo user studies using AmazonMechanical Turk1. The domains andtasks in these user studies are diverse, with one involving searchingfor airline tickets and in the other for healthy (daily) meal plans. Inboth studies, test subjects read a short scenario and used a randomlychosen retrieval model to find an appropriate item (ticket or mealplan). We ask the following questions:RQ1 Which approach and specific retrieval algorithm is most
effective?RQ2 Are constraints beneficial or harmful?RQ3 How should results be ordered?In this work, we make the following contributions:(1) We cast the item retrieval problem as finding the item with
the most desirable combination of attribute values, and useutility theory to develop a basic model.
SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA S. Wolfe, Y. Zhang
(2) We expand upon the basic retrieval model by developingsubutility models that estimate the utility of every possiblevalue of each attribute.
(3) We develop a Bayesian hierarchical model around our itemutility model so the models’ parameters can be fit to data ofusers’ selections.
(4) We evaluate our novel item retrieval model against the twocommon approaches, Boolean and faceted retrieval, alongwith several models from the literature, in two user studiesin very different domains.
The rest of this paper is organized as follows. In section 2, wereview the related research and introduce the previously publishedmethods included in our study. In Section 3, we introduce an initialretrieval method based on multi-criteria decision-making theoryand a subsequent enhancement to that method. The learning frame-work we later use to tune this model’s parameters is described inSection 4. In Section 5, we detail the design of our two user stud-ies, including the baseline retrieval methods that we include forcomparison. Finally, we present the results of these user studies inSection 6 and present our conclusions in Section 7.
2 RELATEDWORKCommon item retrieval methods use a Boolean retrieval paradigm,either in database-like query or faceted search, with the latter a pop-ular choice with many e-commerce websites. Database researchershave expanded on these approaches while preserving clear retrievalsemantics, notably with top-K approaches [16], which retrieve thek-highest scored items given a scoring formula, and ranking givenuncertain data [29]. Skyline queries [15] do not use a specific scoringformula, instead returning the Pareto set given desired characteris-tics. Finally, several researchers have explored incorporating pref-erences into database queries [1, 11, 17, 18]. The focus of that workhas been the semantics of the operators and on efficient execution,and not inferring latent preferences. Overall, the important bodyof work referenced above is focused on a different problem thanours, namely efficiency and defining explicit retrieval semantics,not query intent. In contrast, we do not assume a scoring functionor explicit retrieval paradigm, and instead attempt to maximize usersatisfaction by estimating item relevance.
The few item retrieval methods that do rank results accordingto estimated relevance tend to use methods suited for categori-cal data on all attributes, even numeric ones, perhaps because ofthe similarity to the bag-of-words model of information retrieval.Chauduri et al. [10] and Su et al. [30] adapted the binary inde-pendence model, discretizing numeric attribute values, similar tofaceted search. Agrawal et al. [2] adapted TF*IDF to search databaserecords, but abandoned the term frequency term. AIMQ [22] furtheradvanced the numerical relevance concept through a “like” operatorthat calculated the bounded absolute percentage difference betweenquery and data attributes, combining them in a linear combination.Agrawal et al.’s method and AIMQ were combined and slightlymodified by Meng et al. [20]. CQAds use a normalized absolute dif-ference to compare numerical query and data attributes, combinedin a simple summation, to find advertisements (or more precisely,search through “for sale” listings). Finally, the appropriately named
VAGUE system [21] was an early retrieval framework that incorpo-rated a “similar-to” operator that would retrieve records close to thedesired attribute values, using the system designer’s chosen metricfunction. Vague queries were later incorporated into a probabilisticframework [13], although how to estimate these probabilities wasleft as a difficult open question. We include Agrawal et al.’s, model,AIMQ, CQAds and VAGUE as baselines in our experiment and givetheir mathematical formulations in Sec. 5.2.
The healthy meal plan user study can be seen as a packageretrieval task (retrieving a composite item instead of individualitems), though this is not a central aspect of that study. Prior re-search has focused on recommending packages that meet the user’sconstraints and maximizing a provided objective function. Packagerecommendation has been explored in a number of areas, suchas trip planning [4, 14, 31, 33], student course planning [23–25],compatible products [5], diversity in restaurants [3] and web pageconglomeration [7]. Given the large number of potential pack-ages, recommended packages are typically generated on the fly,typically an NP-complete problem. Our meal plan retrieval studycontrasts with these by selecting from a fixed (though large) corpusof packages and eschewing constraints and objective functions forestimated utility.
We may be the first to apply multi-criteria decision conceptsspecifically to item retrieval, but others have adapted it to generalinformation retrieval, primarily in information filtering. Manouselisand Costopoulou categorize 37 recommender systems that implic-itly use some multi-criteria aspect in their operation [19]. PENG[6, 26] is a multi-criteria news bulletin filtering system that utilizesseveral criteria, including content, coverage, reliability, novelty andtimeliness.
3 TOWARDS A MODEL OF UTILITYIn contrast with the retrieval methods we surveyed in the literature,we develop a retrieval method with clear theoretical justification,building on multi-criteria decision making. In multi-criteria deci-sion making, a decision maker must choose among several candi-dates, with each candidate evaluated by the decision maker on thesame set of criteria. A simple example is choosing a hotel, factoringin price, location, amenities, etc. The decision making process ismade difficult by conflicts among the criteria, that is, when indi-vidual criteria rank options differently, and in particular, when nocandidate is rated highest by all the criteria. Multi-criteria deci-sion making approaches typically rank candidates given a ratingon each attribute value; our problem is even harder as we mustalso estimate subutility functions to rate the attribute values. Theratings on each criterion are typically scaled to lie on a 0-1 scale(with higher ratings preferable). Ratings on different criteria are notassumed to be of equal importance, so a weight for each criterionis usually assigned to capture relative importance, with the sum ofweights equal to 1.
According to multi-attribute utility theory (MAUT) [12], certainassumptions on the properties of preferences entails that the under-lying utility function follow a particular form. We assume mutualutility independence, which means for any subset of attributes, thestrength of preference for a set of values is unaffected by the values
Item Retrieval as Utility Estimation SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA
of other attributes. As an example, this would mean that the dif-ference in utility between two 20 inch computer monitors, priced$150 and $200 but otherwise identical, is the same as the differencein utility between two 25 inch computer monitors, priced $150and $200 but otherwise identical. This assumption entails that theunderlying utility function must be linear combination of ratings,yielding our base utility function:
f (Q,Di ) =∑jw j � дj (qj ,di j ) (1)
where j is the index of the jth attribute,w j is the priority (weight)given to the attribute, Q are the desired attribute values, and Diis the ith item in corpus D, with qj and di j the values of the jthattribute of Q and Di , respectively. дj (qj ,di j ) is the subutility func-tion which evaluates the utility of attribute value di j when the userdesired qj . Items are ranked in order of decreasing utility.
At this point, MAUT offers no further guidance. Subutility eval-uations are taken as input to the MAUT problem, but we need toestimate subutilities. Estimation is trivial for Boolean attributes,as there are only two possible attribute values, so the subutilityis one when qj = di j , zero otherwise. Categorical attributes (e.g.,color) are more challenging. An extreme solution would be to usethe same approach as Boolean attributes, estimating zero subutilityexcept when qj = di j . A more nuanced approach could be derivedfrom domain theory or user choice training data when either isavailable.
Numeric attributes, on the other hand, have mathematical re-lationships among their values which suggest other avenues forsubutility estimation. A simple yet intuitive method is to relatesubutility to the absolute difference from the desired value, whichwe chose as follows:
д(qj ,di j ) =
(1 −
|qj − di j |
max(|qj − ⊥j |, |qj − ⊤j |
) ) (2)
where ⊥j and ⊤j are the least and greatest values of the jth at-tribute in the corpus, and other variables are as defined in Eq. 1.This formulation gives us our initial retrieval model, SimpleMAUT.SimpleMAUT accepts as input a query consisting of desired at-tribute values (0 or 1 per attribute) and attribute priorities (0 whenthe corresponding attribute is not of interest) and returns itemsranked by their estimated utility. SimpleMAUT (and the forthcom-ing MAUT models) could also be extended to support ranges ormultiple desired values by giving such maximum utility.
However, the subutility estimation of numeric attributes in Sim-pleMAUT has several limitations. First, the attribute ratings arenormalized by the extreme attribute values of the corpus, and socan be radically affected by corpus changes. Second, it assumesa linear relationship between the attribute subutility and the at-tribute value, implying a constant rate of subutility change. Thishas nonintuitive consequences, for instance, it implies that a $5discount is just a compelling when applied to $1000 item as it isfor a $10 item. Finally, as is, SimpleMAUT does not have way toincorporate the subutilities of a multiply-valued attribute, whichwe needed for the ratings of multiple dishes in our meal plan userstudy.
We made several changes in an enhanced version of our model,normalizing numeric attribute subutilities with the standard de-viation and including a scaling factor for each subutility. We alsodeveloped a more flexible subutility function based on several prin-ciples. First, the desired value should have maximal subutility. Sec-ond, subutility should never increase as the absolute difference tothe desired value increases. Finally, the subutility function shouldbe as flexible as possible with a minimum number of parameters.Accordingly, we used an exponential function, raised to a positiveexponent, as our subutility function. It can capture a variety offunctions, from a point-like subutility, to gradually diminishinglosses, to a bell-shape curve, and even to a boxcar function in thelimit. The enhanced model has separate subutility function param-eter values above and below the desired value, so that asymmetricsubutilities can be modeled.
We can now present the revised numeric subutility function usedby our enhanced retrieval algorithm, EnhancedMAUT :
where σj is the standard deviation of jth attribute, [ ] is the Iversonbracket, ρ≥ , ρ< , ϕ≥ , ϕ< , and w are model parameters (all 1 in ourexperiment), and others are defined as above.
Finally, we chose to aggregate multiply valued subutilites witha generalized mean, which only applied to the rating of the com-ponent dishes in the meal plan user study. The generalized meantakes a single parameterψ and its argument, a series of numbersx1, ...,xn :
M(x1, ...,xn ) =
[1n
n∑i=1
xψi
] 1ψ
(4)
The generalized mean’s appeal comes from its flexibility, as particu-lar values ofψ will produce the arithmetic, geometric, and harmonicmeans, as well as minimum and maximum. Thus, this one functionallows us to model several reasonable ways a user might evaluate aset of items. In our case, each xi is the estimated subutility of therating of a dish in a meal plan.
4 LEARNINGThe LearnedMAUT model (Figure 1) has the same formulation asEnhancedMAUT, but uses tuned model parameter values for theattribute weights and shapes of the subutility functions, as describedbelow. These are learned in a pairwise learning to rank frameworkwith Bayesian logistic regression, by placing a logistic function ina hierarchical model. Given the utility function f () in Eq. 1, usingthe subutility function д() in Eq. 3, and the general mean (for dishratings only) in Eq. 4, the likelihood function L() is:
SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA S. Wolfe, Y. Zhang
yi
ρ≥ ρ< ϕ≥ ϕ< w
ψ
λ≥ρ λ<ρ λ≥ϕ λ<ϕ
j
i pairs
Figure 1: Hierarchical Bayesian model of LearnedMAUT
-1.0 -0.5 0.0 0.5 1.0
-10
-8-6
-4-2
0
Original Function
score difference
log
likel
ihoo
d
-1.0 -0.5 0.0 0.5 1.0
-2.0
-1.5
-1.0
-0.5
Revised Function
score difference
log
likel
ihoo
d
Figure 2: Log Likelihood
L(ρ≥, ρ< ,ϕ≥,ϕ< ,w,ψ ;Q,D,R,U) =∏Q∈Q
∏r ∈R
∏u ∈U
(b
2+
1 − b
1 + exp(−c(f (Q,Dr ) − f (Q,Du )))
)(5)
where Q are the set of queries, R are the item indices chosen forquery Q, U are the indices of items not chosen for query Q, b andc are tuning parameters, with others defined above. Parameter b(arbitrarily set to e−2 in our experiment, and discussed below) limitsthemaximum loss from any pair, and c (10 in our experiment) affectsgradient smoothness, with results insensitive to small changes ineither parameter.
The model parameters ρ≥ , ρ< , ϕ≥ , ϕ< ,w andψ are given priordistributions, withψ modeled as a standard normal distribution andthe rest modeled with gamma distributions. The hyperpriors λ≥ρ ,λ<ρ , λ≥ϕ , and λ
<ϕ are used to control the modes of ρ≥ , ρ< , ϕ≥ , ϕ< ,
and are modeled as a modified gamma distribution that correctsfor a drift towards more compact distributions with smaller modes.These hyperpriors and w were given a mode of 1. The gammadistributions’ parameters were calculated to fit the mode and givegood regularization.
The tuning parameter b was included to limit model sensitivityto highly unlikely pairs. Initially this parameter was not included
(equivalently given a value of zero), yielding a more conventional lo-gistic function, but we found the probabilisticmodel would gravitatetowards fits where most pairs were slightly unlikely yet resulting ina better overall probability than results with high classification ac-curacy but with a few very unlikely pairs. Our solution was to haveour utility function only describe part of the data, modeling the dataas a mixture of two processes, the other being a random selectionmodel. This also admits uncertainty into the model; at times, a usermay select a different item due to factors that are not captured bythe model. Figure 2 compares how the log likelihood changes for asingle pairwise comparison as their score difference changes, in theoriginal and revised formulation; note also the difference in scale.Since the overall log likelihood is the sum of each pair’s likelihood,it is easy to see that the revised likelihood corresponds much betterwith the overall classification accuracy. For our experiments, wearbitrarily set the mixing parameter b to e−2 (≈ 0.14), noting thatresults were insensitive to small changes in this parameter.
We used the Metropolis-Hastings algorithm to generate samplesfrom the posterior distribution, using the observed modes as themodel parameter values. After the user study, the initial queriesand final selections from that study were separated into 20 folds(for cross-validation), training a separate model for each fold, usingthe other 19 folds for training data and the fold’s data for testing.We partitioned the data into folds by scenario, to prevent selectionsfrom the test scenario biasing the model. However, given the dif-ference in scenarios, queries and limited number of selections, thelearning problem is fairly difficult. We evaluate the learned modelin Section 6 using the mode of the resulting posterior distributions.
5 EXPERIMENTWe conducted two user studies using different domains and severalretrieval models to compare themodels’ ranking of results. After thefirst user study, for our second study (with meal plans) we improvedthe experimental protocol (adding a head-to-head comparison), thebaseline models (replacing Tradeoff with Faceted), and our retrievalmodel (using EnhancedMAUT instead of SimpleMAUT ).
5.1 User InterfacesWe developed different user interfaces to support models withdifferent query input (e.g., some supported ranges, some acceptedsort orders, etc.). Some retrieval models had identical query inputand differed only in the subsequent ranking.
Sorted Boolean For the ticketing user study, we allowed usersto restrict the result set by attribute ranges (with the excep-tion of price) and give a single sort order, as with populartravel sites at the time of development. In the meal planuser study, we allowed restriction on any attribute and upto four sort orders. An example of the query interface in theticketing user study is shown in Figure 3.
Faceted For themeal plan user study, we created a basic facetedsearch model. All attributes were split into a small number ofequally sized facets, with seven to twelve facets per attribute.Up to four sort orders could also be chosen. An example ofthe query interface is shown in Figure 4.
Point The point-based user interface allows a user to specifysingle values for each attribute, allowing the user to give
Item Retrieval as Utility Estimation SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA
Figure 4: Faceted search interface, menu plan study.
specific attribute values of interest. Partial specifications(attributes can be left blank) are acceptable, as with other in-terfaces. This interface was used for theMAUT-basedmodelsas well as baselines from the literature.
Tradeoff As our retrieval model was based on an implicit util-ity function, we created an alternative where users couldprovide a simple utility function, as shown in Figure 6. Aswith the Point interface above, users could specify desiredattribute values, but also provide exchange rates for eachunit change, for instance in Figure 6, each connection added
Figure 5: Point-based search interface, menu plan study.
Figure 6: Utility function search interface, ticket study.
to a flight is acceptable only if it saves $100 (or more). The in-terface was only used by the Tradeoff model in the ticketinguser study, which is described in the following section.
5.2 Retrieval ModelsWe used our proposed models, the de facto item retrieval methodsof Boolean and faceted search, as well as several models from theliterature (see Sec. 2), as listed below. Variables are defined as inSection 3 unless otherwise specified.
SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA S. Wolfe, Y. Zhang
AIMQ AIMQ estimates relevance using a different normaliza-tion and global weights derived from functional dependen-cies (see [22] for details). Items are ranked by decreasingscore order, where the score of di is defined as:
f (Q,Di ) =∑j ∈Q
w j
(1 −min
(1,
|qj − di j |
qj
))(6)
where j ∈ Q indicates the jth attribute was given a desiredvalue (not left blank), andw j is the global weight for the jthattribute.
AutoRank This is the (unnamed) model of Agrawal et al. Theyused an inverse document frequency (IDF) term for weight-ing, defined below for query element qj as:
where n is the number of items, and hj is a “bandwidth”parameter, chosen by Agrawal as hj = 1.06σjn−
15 . This is
combined in their overall scoring function:
f (Q,Di ) =∑j ∈Q
w j exp
[−12
(di j − qj
hj
)2](8)
with items ranked by decreasing score.CQAds CQAds estimates relevance much like AIMQ, but with
a different normalization, andwithout attribute-specificweights.In our adaption of CQAds scoring, items are ranked by de-creasing score order, where the score of di is
f (Q,Di ) =∑j ∈Q
(1 −
|qj − di j |
Rj
)(9)
where Rj is an estimation of the range of the jth attribute,defined as the mean of the ten greatest values minus themean of the ten least values.
Faceted/Sorted Boolean Faceted search and Sorted Booleansearch have the same retrieval semantics with different userinterfaces. Items that meet the constraints given by the userare returned and ordered by any provided sort orders.
MAUTs SimpleMAUT and EnhancedMAUT were described inSection 3, whereas LearnedMAUT was described in Section4. SimpleMAUT was used only in the ticketing user studyand included user provided attribute priorities, whereas En-hancedMAUT was used in the meal plan study with uniformattribute weights. LearnedMAUT was trained and evaluatedpost-hoc on data from both user studies.
Tradeoff Contrasting with SimpleMAUT, the Tradeoff modelallowed test subject to directly provide a utility functionby giving an explicit tradeoff rate (in terms of dollars) theywould be willing to spend to get closer to their desired at-tribute values. Items are ranked by increasing score order,where the score of Di is defined as:
f (Q,Di ) =∑jtj |qj − di j | (10)
where tj is the tradeoff rate (in dollars) for the jth attribute.VAGUE The VAGUE framework provides “similar-to” operator
that calculates a weighted Euclidean distance from the querypoint and the item. The operator can use subutility functions,but none are prescribed, so we choose the absolute differencedivided by the standard deviation:
f (Q,Di ) =
√√√∑j
[w j
(|qj − di j |
σj
)]2(11)
with items ranked by increasing score. As with the MAUTsabove, user provided attribute priorities were used in theticketing study and replaced with uniform weights in themeal plan study.
Only EnhancedMAUT and LearnedMAUT were developed tosupport multiply-valued attributes (our meal plans have a separaterating for each included dish), so we use the (arithmetic) mean toaggregate such multiple values in the experiment, except whereotherwise noted.
AIMQ,CQAds, theMAUT-basedmodels (EnhancedMAUT, Learned-MAUT, SimpleMAUT ), and VAGUE all accept the same query input,differing only in how they rank results. Thus, only SimpleMAUTand EnhancedMAUT were used during the user study, with the oth-ers evaluated post hoc using only the first query from each session,as subsequent queries are influenced by the search engine actuallyused. Additionally, LearnedMAUT was trained with data after userstudy completion instead of on-line.
5.3 Data UsedFor both user studies, we wanted test subjects to perform realistictasks, using appropriate real world data. We developed twenty shortscenarios for each study based on the literature.
We're meeting with a potential new distributor forfour days in Omaha, starting Monday, January 9, andending Thursday, January 12. I need to leave Sunday,January 8 to get there on time, and have to leave nolater than 2 PM on January 12. I'm giving a presentationabout the meeting to the directors in Burbank at 8 AMFriday, January 13. It's business travel, so I won't bepaying for the ticket.
Figure 7: Example Scenario for Ticketing Study
For the ticketing study, we consulted a survey from more than26,000 U.S. households to capture who travels by air and the reasonswhy [9], using this breakdown to develop 10 scenarios for pleasure,8 for business, and 2 for personal business. To make the scenariosslightly more compelling, we created somewhat vague reasons forthe trip (i.e., “attend a meeting”, “visit relatives”, “take a vacation”).We chose arbitrary dates to match the scenarios, with personal tripssomewhat longer in duration and with random time constraints(from 9 AM to 4 PM) for business trips, ranging from nearly trivialto quite restrictive. For half of the remaining scenarios, we listedother criteria (such as “get home early”), while leaving the othersopen-ended. Figure 7 shows a scenario with constraints for business
Item Retrieval as Utility Estimation SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA
travel. Finally, we also sampled demographic information from thesame survey (gender, age, income) to give more context. To buildthe ticket corpus, we randomly selected origin/destinations pairsfrom a sample of U.S. domestic travel in 2006 [8] and retrievedtickets from Expedia (using dates of our choosing) with the sameorigin and destination, which at that time yielded approximately60 round-trip tickets for each scenario, yielding a separate corpusfor each scenario. Each ticket was described by nine attributes(e.g., price, outbound departure, etc., as in the ideal ticket in Figure6). Dates and origin/destination were treated as unalterable hardconstraints.
You are choosing meals for Emma, a 30 year old female.Emma is concerned about her fat intake. She has read thatat most 30% of calories should come from fat, with at most10% coming from saturated fat. Emma wants a daily mealplan that follows the nutritional recommendations, withan emphasis on delicious food, calories from fat, totalfat, and saturated fat.
Figure 8: Example Scenario for Meal Plan Study
For the meal plan study, we consulted a popular nutritional re-source [27] which tabulated nutritional needs by age and gender,as well as modifications needed for various diseases and lifestyles.In addition to these specific recommendations, we also included adesired nutritional range in the form of Estimated Average Require-ment and Tolerable Upper Limit [32] when such are defined. Wedeveloped twenty core scenarios, choosing a variety of conditions,genders, and ages. In addition, four meal plan attributes (tastinessand three randomly selected nutrients, typically overlapping withany nutritional modifications) were emphasized to focus the testsubject. In all, 119 scenarios were generated during the user study.Figure 8 gives an example of one of the meal plan scenarios.
We used the meal plan components (individual dishes) to createthe corpus, as large collections of daily meal plans are not common.We downloaded roughly fifty thousand recipes from the recipe-sharing website allrecipes.com to serve as the building blocks ofour meal plan corpus. Allrecipes.com recipes include a variety ofmetadata (such as type of dish, meal, and cuisine) and nutritionalinfo which made it ideal for building daily meal plans. From this,we used a meal plan generator that selects appropriate main dishesfor breakfast, lunch and dinner, adding additional meal components(side dishes, drinks, appetizers and desserts) with decreasing proba-bility as the daily calorie count increases, creating approximately aquartermillionmeal plans. Twenty of the attributes were nutritionalinformation (e.g., calories, vitamin A, etc, as in Figure 5) which couldbe simply summed. The other attribute was allrecipe.com individualdish ratings, which were preserved for each meal plan.
5.4 Subject’s tasks and rewardsWe developed a game with rewards to motivate test subjects totake the task seriously and put effort into choosing the best items.We used Amazon Turk workers as our test subjects, restricting toworkers within the United States and with high completed workacceptance rates (95% or better). The game was slightly different
in each user study but followed the same basic structure. Severalworkers would be given the same scenario andwere asked to choosethe selection(s) that would be most likely to please the persondescribed in the scenario. There were two roles, the searcher andjudge, as described below:
Searcher This role was used to generate queries and relevance judg-ments. The searcher used a randomly selected search en-gine to search the corpus and select items. These selectionswere entered into a “contest” and assigned a judge, with thesearcher receiving a bonus if their selection won the contest,as described below. For the ticketing user study, three ticketswere selected; only one meal plan was selected in the mealplan user study.
Judge This role was used to validate work and provide bonuses.The judge selects items from a randomly ordered list withoutthe benefit of a search engine. The judge would see a smallsubset of items that included the selection(s) of the searcher;if the judge chose the worker’s selection, the worker wouldget a bonus. A second judge would be given the same set tojudge, and if they made the same selection, they would bothget a reward. study was altered to include only selectionsfrom two searchers using different search engines and tworandomly selected meal plans, so the results from differentsearch engines could be directly compared (head-to-head).
In addition, the meal plan user study asked each test subject toprovide a justification for their selection. Work was rejected whenjustifications were inadequate and eliminated from our study.
Table 1: User Study Data
User Test Tasks Initial ItemsStudy Subjects Completed Queries Chosen
We excluded roughly half of the searcher responses (completedtasks) from the ticketing user study to eliminate noisy data as fol-lows. For each scenario, we calculated the median probability ofbeing matched with another searcher who selected at least onecommon ticket, discarding all responses that fell below this median.The two groups (discarded and preserved) showed a statisticallysignificant difference on time spent according to a randomizationtest (also described below) at p=0.05. In the meal plan study, werejected responses with inadequate justifications, eliminating about10% of the responses. Table 1 summarizes the data for each userstudy; only the initial query is used in our analysis, thus its countis equal to the number of responses. Likewise, subjects were in-structed to pick exactly three tickets and only one meal plan in thecorresponding study.
5.5 Evaluation MetricsAs mentioned in Sec. 5.2, we use only the first query from eachsession to calculate mean average precision (MAP), precision atk (P@k), and mean reciprocal rank (MRR). However, a test sub-ject may be likely to choose a higher ranked item when utility is
SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA S. Wolfe, Y. Zhang
roughly the same. Moreover, given a large number of items, theitem ultimately chosen is affected by the retrieval model’s rankingas not all items will be viewed. This is irrelevant when comparingthe retrieval models used directly by test subjects, but could bias theresults when comparing retrieval models in our post hoc analysis.To compensate, we break the bond between the test subject’s queryand ultimate selections by using the selections from the other testsubjects on the same scenario, which we refer to as the communityevaluation. For the ticketing user study, we combined these choicesinto a single MAP evaluation since all users were given all thetickets, given the small corpus. For the meal plan user study, thecombined set of search results from any test subject’s session wassuch a small fraction of the corpus that there was little to no overlapamong sessions. Therefore, each query was evaluated separatelyon each result set. We further only used result sets from queriesthat won at least the median number of contests.
As differences in response and acceptance rates per scenario gaveus varying amounts of data, we average our results in two ways.The first is the micro-average, which is the mean over all responseswithout respect to the scenario. The second is the macro-average,which calculates an overall average from the mean of each scenarioindividually. The macro-average compensates for an unbalanceddistribution but may have higher variability, as scenarios with fewerresponses are weighted the same as those with more responses.
We use a randomization test[28] in two ways to calculate statisti-cal significance. The first method is usedwhen comparing responsesby different subjects in the user study, using in-study models; nosubject was allowed to respond to the same scenario more thanonce. Our null hypothesis is that each test subject would have hadthe same performance on either of the compared models, and sothe observed difference is merely a chance event stemming fromrandom assignment of test subjects. The second method is used inour post-study model evaluation. Here our null hypothesis is eachmethod was equally likely to have produced the observed difference.To test the null hypothesis, we randomly redistribute the responsesor the differences, respectively, within the scenarios among the twomodels one million times. The p-value is the fraction of times thisredistribution produced a difference for the metric that was at leastas great as the actual observation. We used the same simulation runto calculate the p-value for all metrics jointly (e.g., instead of usingBonferroni’s correction), and found the p-values reported belowhold for each family as well.
6 RESULTSWe provide various results of our experiment below, with the leaderbolded and statistically significant difference (at p=0.05 or better)indicated by the dagger (†), with the overall best performancebolded. Table 2 shows the results of the ticketing user study with thesearcher’s own selections. The SimpleMAUT model outperformedall others by a statistically significant difference, and the SortedBoolean model, despite its widespread use, performed the worst.Table 3 shows the results for community-judged MAP, calculatedfrom the other test subject’s ticket selections (see Section 5.5), withthe maximum a posteriori estimate of model parameters used forthe LearnedMAUT model. All models except Autorank performed
relatively well, with LearnedMAUT performing the best, mostly bystatistically significant differences.
Table 4 shows results on scenarios with explicit constraints (35%of the ticketing scenarios), again with the searcher’s own selections.Surprisingly, though restricting results is more effective on thesescenarios, Sorted Boolean is still outperformed by the unconstrainedSimpleMAUT. Table 5 shows why; approximately a third of thefinal selections had been eliminated by the test subjects’ initialconstraints. Though restricting the result set was more effectiveeliminating unwanted choices from the constrained scenarios, asexpected, it also eliminated final selections at almost the exactsame rate for both constrained and unconstrained scenarios. Thisfurther demonstrates the the hazard of using hard constraints toapproximate soft preferences, even when the user need also has hardconstraints.
Table 6 gives the community-evaluated MAP scores for modelsin the meal plan user study. Qualitatively, the results are similar tothe ticketing user study despite differences in the domain and cor-pus size, with the LearnedMAUT model outperforming the others.A unique feature of the meal plan domain was the multivalued dishrating attribute, which we aggregate with a generalized mean. Thevalue ofψ in our experiment was close to the geometric mean (av-eraging around -0.25 and varying by fold, where a value of 0 yieldsthe geometric mean). Changing the baselines to use the geometricmean (instead of arithmetic as shown in Table 6) yielded betterresults, mostly by a statistically significant differences; even so, thedifferences with the LearnedMAUT result remained statisticallysignificant.
Another way to evaluate search result quality is to see howoften a model was used to find the winning meal plan. Table 7shows searchers using the EnhancedMAUT search engine werevery successful, beating the competition (i.e., searchers using adifferent search engine) nearly two thirds of the time. Moreover, ifwe use the search engine to rank the contest entries (given as “JudgeMRR”), the advantage of the EnhancedMAUT model is even clearer.A direct comparison is given in the “head-to-head” performance inTable 8, with each row in the table listing the “victories” in matchesbetween the pair of search engines in the columns. For example,the EnhancedMAUT and Faceted search engines have competed 53times (i.e., entered into the same contest, as described in Section5.4), with the EnhancedMAUT paradigm winning 35 contests andlosing 18. As with the other comparisons, the difference betweenEnhancedMAUT and the others is statistically significant.
Explicitly constraining the result set hurt the performance ofSorted Boolean and Faceted (RQ2). Hard constraints are not well-suited to expressing preferences, and we found that test subjectsoften ultimately selected items that were eliminated by their ini-tial restrictions. Though the user interfaces for Sorted Boolean andFaceted are quite different, the underlying query semantics are iden-tical, and we observed nearly identical retrieval performance. Onthe other hand, the Tradeoff model had a very similar ranking func-tion to SimpleMAUT, but with explicit utility function parameters,and this lead to poor performance. Indeed, models that allowedusers to give an explicit ranking (Sorted Boolean, Faceted and Trade-off ) performed worse than the models that implicitly ranked byattempting to glean query intent (RQ3). Overall, test subjects weremost successful using the implicit MAUT query models (RQ1).
Item Retrieval as Utility Estimation SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA
Table 2: In-Study Model Results on Ticketing User Study
The models that accept single attribute values instead of ranges(AIMQ, AutoRank, CQAds, MAUT variants, Tradeoff and VAGUE)vary widely in their performance, despite their similarities. AIMQ,AutoRank and CQAds did not use the user attribute prioritizations,whereas VAGUE and SimpleMAUT did. However, further experimen-tation showed that the user attribute prioritizations (versus uniformprioritizations) convey only a slight advantage that was not statisti-cally significant; thus we dropped user attribute prioritizations from
Table 7: Meal Plans: Searcher Success by Search Engine
Paradigm Win Rate Judge MRR
EnhancedMAUT 0.61† 0.65†
Faceted 0.44 0.17Sorted Boolean 0.45 0.11
Table 8: Meal Plans: Head-to-Head
Enhanced SortedMAUT Faceted Boolean
54† 2935† 18
23 25
the EnhancedMAUT /LearnedMAUT model. AIMQ and AutoRanksuffered because of their estimated attribute weights; replacingthese with uniform weights improved performance. In contrast,LearnedMAUT was significantly better than other models in everycategory (RQ1). Overall, the research baseline models suffered asthey did not have a way to adjust to new domains of application.They did not learn from usage, either adjusting their parametersbased from an analysis of the corpus or from assumptions. Thismay have worked in domains used in their development, but notelsewhere. In contrast, we have trained and tested LearnedMAUTin two disparate domains, with it performing well in both.
7 CONCLUSIONSIn this paper, we explored the retrieval of items solely by theirattribute values in two user studies conducted with Amazon Me-chanical Turk. We proposed two models derived from multi-criteriadecision making theory, a basic model SimpleMAUT and an ad-vanced version, EnhancedMAUT, whose model parameters weretuned in a learning-to-rank framework, LearnedMAUT. We com-pared these to the de facto explicit retrieval models, where the userexplicitly describes what to return and how to order it. We alsocompared our methods to several implicit retrieval models found inthe literature, where retrieval and ranking is implied by the user’sdescription of what is desired.
Our models outperformed these widely adopted explicit retrievalmodels. We analyzed the performance of the explicit retrieval mod-els to understand why they did not perform better. Applying con-straints to limit the result set is hazard-prone; the mismatch be-tween constraints and preferences often eliminates the desired
SIGIR ’18, July 8–12, 2018, Ann Arbor, MI, USA S. Wolfe, Y. Zhang
items. Overall, users were more successful when providing an im-plicit ranking rather than explicitly describing the result set (eitherwith constraints and sorting or by providing a utility function, as inthe Tradeoff model). This largely matches the findings of decadesof research in unstructured text retrieval, so retrieving items byattribute may not be as different as presumed. Nonetheless, not allimplicit retrieval models are equivalent, in particular the MAUT-based models outperformed these baselines in our experiments. Thebaseline implicit models made different assumptions, notably innormalization and weighting, that did not always hold in practice.
We were able to learn a better retrieval model using a pairwiselearning-to-rank approach, yet numerous possibilities remain. Anal-ysis of query performance showed that underspecification was of-ten the cause of poor retrieval performance, so enhancing retrievalwith universal preferences could improve results. Also, we learneda global retrieval model, but personalized models and interactiveretrieval are also promising avenues. Finally, we assumed mutualutility independence, but interaction among attributes should alsobe explored.
We developed a multi-attribute utility model to solve a particularproblem, namely retrieving items by their attribute value. This is acommon task itself, but the framework we developed could be ap-plied to other retrieval problems with multiple dimensions, such asincorporating diversity or recency into document retrieval, match-ing subgraphs in semantic search, and so on. Casting retrieval asutility estimation provides a new way to think about the problem;multi-criteria decision making offers a technique for combiningmultiple evaluations; and our subutility functions translate raw fea-tures into utility estimates, fitting a variety of possible preferences.
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