PATCH’07, 26.06.07 UM’07, Corfu, Greece “A Framework for Guiding the Museum Tour...

PATCH’07, 26.06.07UM’07, Corfu, Greece

“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders

1

A Framework for Guiding A Framework for Guiding the Museum Tour Personalizationthe Museum Tour Personalization

Mykola Pechenizkiy, Toon Calders

Information Systems GroupDepartment of Computer

ScienceEindhoven University of

Technologythe Netherlands

PATCH Workshop, UM’07, Corfu, Greece June 25, 2007



2

Outline Introduction

– Motivation and goals Personalization and adaptation of the cultural heritage content

– Personalization process– The basic approaches for personalization– Nonintrusiveness: efficient learning of user preferences– What is special in personalization of access to the museum

artworks? The generic framework: Optimally Personalized Museum Tour

– Formal description of the museum tour personalization Evaluation methodologies for personalization

– Challenge of Scientific Evaluation of Personalization The methodological framework for evaluating and guiding

personalization process Discussions and further research



3

Motivation Information overload - too much content!

– Too many artworks to see them all at one visit– Diversity of content and diversity of visitors’ needs

Web-access to museums collections– Introduce the existing galleries, collections,

artworks– Educate virtual visitors– Recommend virtual visitors what they may want

to do in the museum • Suitable galleries, collections, or personalized tours

CHIP “I know what you’ll see in the museum next

<Sunday, month, summer, …>”



4

Goals

to tailor personalized access to a visitor’s

(potentially changing) interests and preferences

without demanding to express them explicitly and

without increasing visitor’s intrusiveness.

– Interest vs. interests: coverage

– Recommending a tour, not an individual artwork.

to start offering the most relevant information

(recommendations) to the (possibly first-time)

visitors as soon as possible while trying to

minimize the users’ intrusion.



5

Sources for the introductory slides

– “AI Techniques for Personalized Recommendations” IJCAI’03 Tutorial by Konstan et al.

– “Comparing Human Recommenders to Online Systems” by Rashmi Sinha & Kirsten Swearingen

– “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions” and

– “Personalization Technologies: A Process-oriented Perspective” by G. Adomavicius and A. Tuzhilin



6

Personalization process

Customization (adaptable) vs. personalization

(adaptive)

– customization (or adaptability) assumes active user active user

participation participation (a visitor has a possibility to configure the

adjustable properties of the application) and explicit inputexplicit input

(manually creating and/or editing an own profile).

– In personalized and adaptive applications not a visitor, but

the system is responsible for automatic personalization system is responsible for automatic personalization

of structureof structure, content and its outlook according to visitor’s

preferences, which can be either also learnt by the system learnt by the system

automaticallyautomatically, or, alternatively, the necessary information

can be explicitly provided by the visitor.explicitly provided by the visitor.



7

Personalization process

understandingunderstanding, who is the user and what kind of

content is of his or her interest, through user

modelling process that often consists of some

relevant data collection, its analysis and

transformation to actionable knowledge;

deliveringdelivering the personalized content,

measuring and evaluatingmeasuring and evaluating the impact of

personalization on the visitor’s satisfaction in

particular and on achieving goals defined by the

resources provider in general



8

Recommendation Process

Understand the visitors

Deliver personalized

content

Measure the personalization

impact



9

Recommendation Process



10

Consumers vs. providers Provider-centric

– Show people what they will buy– Learn what people want so you

have it– Learn how much they want it so

you charge as much as possible

User-centric– Find what I want– Know I will like it– Trust system to help me– Team up with my friends to

defeat evil marketers



11

Key Problems Gathering “known” ratings for matrix

– Explicit• Ask people to rate items (what items?)

– Implicit• Learn ratings from user actions

Extrapolate unknown ratings from known ratings– Mainly interested in high unknown ratings– Key problem: matrix of ratings is sparse

• most people have not rated most items, unless it is a controlled experiment or aka pre-test for evaluation of users tastes

– Three groups of approaches• Content-based; Collaborative; Hybrid

Evaluating extrapolation methods



12

Design of Personalization Process

develop good metrics to determine

personalization impact;

study the feedback-integration problem and

develop novel methods to address it;

investigate the goal-driven design process in

order to achieve better personalization

solutions.



13

The basic approaches for The basic approaches for personalizationpersonalization

Content-based methods– analyze the common features among the items I a visitor

rated highly and recommend those items that are similar to I Collaborative-based methods

– search for peers of a visitor that have similar preferences and then recommend those items that were most liked by the peers

• User-to-user or Item-to-item collaborative filtering Hybrid approaches

– combine collaborative and content-based methods• Cascade, parallel, meta

Memory-based algorithms (lazy-learners)– heuristics that can predict ratings based on memorizing and

searching the entire collection of previously rated artworks by the visitors

Model-based algorithms– use the collection of ratings to learn a model, which is then

used to make rating predictions



14

Content-based recommendations

Main idea: recommend items to customer U similar to previous items rated highly by U

Artwork recommendations– recommend artworks with same painter, style, year, etc.– or with “similar” content …



15

Limitations of content-based approach

Finding the appropriate features– i.e., features on paintings themselves as images (not

their annotations) Overspecialization

– Never recommends items outside user’s content profile– People might have multiple interests

Recommendations for new users– How to build a profile?



16

User-user/item-item Collaborative Filtering

Submit/store ratings, compute correlations, request recommendations, identify neighbors, select items, predict rating



17

Limitations of Collaborative Filtering

Collaborative filtering cannot recommend new items: no one has rated them

– Random– Content analysis

Collaborative filtering cannot match new users: they have rated nothing

– Provide average ratings– User agents collect implicit ratings– Put users in categories– Carefully select items for users to rate



18

Five basic types of approaches



19

5 approaches to recommendation and their typical positive (above) and negative (below) aspects,

according to Burke (2002)



20

Hybridization methods

Weighted– The scores (or votes) of several recommendation techniques are

combined together to produce a single recommendation Switching

– The system switches between recommendation techniques depending on the current situation (short−term and long−term models)

Mixed– Recommendations from several different recommenders are

presented at the same time (e.g. Amazon’s web pages) Feature combination

– Features from different recommendation data sources are thrown together into a single recommendation algorithm (CBR)

Cascade– One recommender refines the recommendations given by another

Feature augmentation– Output from one technique is used as an input feature to another

Meta-level– The model learned by one recommender is used as input to

another

Motivation: the various techniques have partly complementary strengths and weaknesses



21

Our current focuses NoninrusivenessNoninrusiveness

– As accurate model as possible in as few rating requests As accurate model as possible in as few rating requests as possibleas possible

Coverage of user interestsCoverage of user interests– If someone is interested in landscaped and also in If someone is interested in landscaped and also in

portraits, but lesser than in landscapes, what happens?portraits, but lesser than in landscapes, what happens? Recommending tour not an individual artworkRecommending tour not an individual artwork

– Implies new challenges and constrainsImplies new challenges and constrains– By now – our focus is coverageBy now – our focus is coverage– In general – many other things are interesting (e.g. In general – many other things are interesting (e.g.

physical placement of artworks)physical placement of artworks)



22

NoninrusivenessNoninrusiveness

Efficient Learning of User Preferences (Active Learning)

ActiveCP approach– utilizes information about items controversy and popularity

VC-WMP algorithm– clusters items by categories in order to reduce the

dimensionality and sparseness of the score matrix and applies a majority vote learner with selection of votes based on the correlation of user profiles

Entropy-driven active learning algorithm– allows to better balance learning efficiency and user

satisfaction Transductive experimental design

– explores available unrated items and selects such items that are on the one side hard-to-predict and on the other side representative for the rest of the items



23

Optimally Personalized Museum Tour

A Generic Framework of the Optimally Personalized Museum Tour Problem

with every object oO , a set of characteristics c(o) is associated:

– the nightwatch: {rembrandt, 17th century, oil paint, militias}

a user u which has a preference u(o) for every object

oO

coverage of the Tour

quality of the Tour

benefit Function

offline vs. online settings



24

Evaluating Predictions

Compare predictions with known ratings– Root-mean-square error (RMSE)

Another approach: 0/1 model– Recall/coverage

• Number of items/users for which system can make predictions– Precision

• Accuracy of predictions– Receiver operating characteristic (ROC)

• Tradeoff curve between false positives and false negatives Narrow focus on accuracy sometimes misses the

point Cautions in data interpretation

– Users may like/”buy” items regardless of recommendations– Users may also avoid seeing certain artworks they might

have seen based on recommendations



25

Relevance Feedback



26

(RE – recommendation engine, RI – recommended items, URF – user relevance feedback

A/B test-based guided personalization



27

Research directions

Enabling Similarity function Confidence in visitors tastes Evaluation Guiding personalization processAdvanced profiling techniques based on data mining

– finding actionable rules, sequential patterns, and signatures

adjust recommendations to the context in which it is offered– take into consideration the when, where, and with whom, etc

contexts into consideration

Track and handle concept drift: – changes due to changes in hidden contexts

• Changing user habits

• Previous history may not accurately predict present tastes in arts



28

Discussion and Further Research developing methods that utilize some of the more

advanced profiling techniques based on data mining

– finding actionable rules, sequential patterns, and signatures

adjust recommendations to the context in which it is offered

– take into consideration the when, where, and with whom, etc contexts into consideration

– hidden contexts: concept drift• Changing user habits• Previous history may not accurately predict present tastes in arts

scientific evaluation of personalization

– high-quality controlled experiments

– fair estimating the benefits and limitations of certain personalization technique



29

Mykola PechenizkiyInformation Systems Group

Department of Computer ScienceEindhoven University of Technology

the NetherlandsE-mail: [email protected]

http://www.win.tue.nl/~mpechen

THANK YOU!

Contact Info

MS Power Point slides of other recent talks andfull texts of selected publications are available online at:http://www.win.tue.nl/~mpechen/talks/talks.html

mailto:[email protected]

http://www.win.tue.nl/~mpechen

http://www.win.tue.nl/~mpechen/talks/talks.html

Date post:	21-Dec-2015
Category:	Documents
View:	216 times
Download:	2 times

PATCH’07, 26.06.07 UM’07, Corfu, Greece “A Framework for Guiding the Museum Tour...

Documents