PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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, …>”
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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.
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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.
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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Recommendation Process
Understand the visitors
Deliver personalized
content
Measure the personalization
impact
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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Recommendation Process
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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.
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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 …
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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?
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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User-user/item-item Collaborative Filtering
Submit/store ratings, compute correlations, request recommendations, identify neighbors, select items, predict rating
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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Five basic types of approaches
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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5 approaches to recommendation and their typical positive (above) and negative (below) aspects,
according to Burke (2002)
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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)
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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Relevance Feedback
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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(RE – recommendation engine, RI – recommended items, URF – user relevance feedback
A/B test-based guided personalization
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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
PATCH’07, 26.06.07UM’07, Corfu, Greece
“A Framework for Guiding the Museum Tour Personalization” by M. Pechenizkiy & T. Calders
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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