vu university amsterdam - the social web 2016 - lecture 5

Social Web2016

Lecture 5 Personalization on the Social Web

Lora Aroyo and Davide Ceolin(some slides adapted from Fabian Abel)

The Network InstituteVU University Amsterdam

theory amp techniques for how to design amp evaluate

recommenders amp user models to use in Social Web applications

Social Web 2016 Lora Aroyo and Davide Ceolin

Fig 1 Functional model of tasks and sub-tasks specifically suited for SASs

Functional model of tasks and sub-tasks specifically suited for SASs (Ilaria Torre 2009)


Kevin Kelly

How to infer amp represent user information that supports a given application or context

User Modeling


bull Application has to obtain understand amp exploit information about the user

bull Information (need amp context) about user

bull Inferring information about user amp representing it so that it can be consumed by the application

bull Data relevant for inferring information about user

User Modeling Challenge


bull People leave traces on the Web and on their computersbull Usage data eg query logs click-through-data bull Social data eg tags (micro-)blog posts comments

bookmarks friend connections bull Documents eg pictures videosbull Personal data eg affiliations locations bull Products applications services - bought used installed

bull Not only a userrsquos behavior but also interactions of other users bull ldquopeople can make statements about merdquobull ldquopeople who are similar to me can reveal information

about merdquobull ldquosocial learningrdquo collaborative recommender systems

User amp Usage Datais Everywhere


bull User Profile = data structure = a characterization of a user at a particular moment represents what from a given (system) perspective there is to know about a user The data in the profile can be explicitly given by user or derived by system

bull User Model = definitions amp rules for the interpretation of observations about the user amp about the translation of that interpretation into the characteristics in a user profile user model is the recipe for obtaining amp interpreting user profiles

bull User Modeling = the process of representing the user

UM Basic Concepts


bull Overlay User Modeling describe user characteristics eg ldquoknowledge of a userrdquo ldquointerests of a userrdquo with respect to ldquoidealrdquo characteristics

bull Customizing user explicitly provides amp adjusts elements of the user profile

bull User model elicitation ask amp observe the user learn amp improve user profile successively ldquointeractive user modelingrdquo

bull Stereotyping stereotypical characteristics to describe a user

bull User Relevance Modeling learninfer probabilities that a given item or concept is relevant for a user

Related scientific conference httpumap2011org Related journal httpumuaiorg

User Modeling Approaches


httpfarm7staticflickrcom62406346803873_e756dd9bae_bjpg

Which approach suits best the conditions of

applications

bull among the oldest user modelsbull used for modeling student

knowledgebull the user is typically

characterized in terms of domain concepts amp hypotheses of the userrsquos knowledge about these concepts in relation to an (ideal) expertrsquos knowledge

bull concept-value pairs

Overlay User Models


bull Ask the user explicitly learnbull NLP intelligent dialoguesbull Bayesian networks Hidden Markov models

bull Observe the user learn bull Logs machine learningbull Clustering classification data mining

bull Interactive user modeling mixture of direct inputs of a user observations and inferences

User Model Elicitation


httphunchcomSocial Web 2016 Lora Aroyo and Davide Ceolin


httpfarm1staticflickrcom155413650229_31ef379b0b_bjpg

bull set of characteristics (eg attribute-value pairs) that describe a group of users

bull user is not assigned to a single stereotype - user profile can feature characteristics of several different stereotypes

User Stereotypes

based on slides from Fabien Abel


Can we infer a Twitter-based User

Profile

User Modeling Building Blocks








Observationsbull Profile characteristics

bull Semantic enrichment solves sparsity problemsbull Profiles change over time recent profiles reflect

better current user demandsbull Temporal patterns weekend profiles differ

significantly from weekday profilesbull Impact on recommendations

bull The more fine-grained the concepts the better the recommendation performance entity-based gt topic-based gt hashtag-based

bull Semantic enrichment improves recommendation quality

bull Time-sensitivity (adapting to trends) improves performance


User Modelingit is not about putting everything in a user profile

it is about making the right choices


User Adaptation

Knowing the user to adapt a system or interfaceto improve the system functionality and user

experience


A Jameson Adaptive interfaces and agents The HCI handbook fundamentals evolving technologies and emerging applications pp 305ndash330 2003

User-Adaptive Systems



Lastfm adapts to your music taste

bull Overfitting ldquobubble effectsrdquo loss of serendipity problem bull systems may adapt too strongly to the

interestsbehaviorbull eg an adaptive radio station may always play the

same or very similar songsbull We search for the right balance between novelty and

relevance for the userbull ldquoLost in Hyperspacerdquo problem

bull when adapting the navigation ndash ie the links on which users can click to findaccess information

bull eg re-orderinghiding of menu items may lead to confusion

Issues in User-Adaptive Systems


What is good user modelling amp

personalisation

httpwwwflickrcomphotosbellarosebyliz4729613108

From the consumer perspective of an adaptive system

From the provider perspective of an adaptive system

Success Perspectives


bull User studies askobserve (selected) people whether you did a good job

bull Log analysis Analyze (click) data and infer whether you did a good job

bull Evaluation of user modelingbull measure quality of profiles directly eg measure

overlap with existing (true) profiles or let people judge the quality of the generated user profiles

bull measure quality of application that exploits the user profile eg apply user modeling strategies in a recommender system

Evaluation Strategies


Evaluating User Modeling in RecSys


Possible Metrics


bull The usual IR metricsbull Precision fraction of retrieved items that are relevantbull Recall fraction of relevant items that have been

retrievedbull F-Measure (harmonic) mean of precision and recall

bull Metrics for evaluating recommendation (rankings)bull Mean Reciprocal Rank (MRR) of first relevant itembull Successk probability that relevant item occurs

within the top kbull If a true ranking is given rank correlations bull Precisionk Recallk amp F-Measurek

bull Metrics for evaluating prediction of user preferencesbull MAE = Mean Absolute Errorbull TrueFalse PositivesNegatives

Predict relevantusefulinteresting items for a given user (in a given context) itrsquos often a ranking task

Recommendation Systems


March 28 2013




httpwwwwiredcommagazine201111mf_artsyall1


Collaborative Filteringbull Memory-based User-Item matrix ratingspreferences of users =gt

compute similarity between users amp recommend items of similar users

bull Model-based Item-Item matrix similarity (eg based on user ratings) between items =gt recommend items that are similar to the ones the user likes

bull Model-based Clustering cluster users according to their preferences =gt recommend items of users that belong to the same cluster

bull Model-based Bayesian networks P(u likes item B | u likes item A) = how likely is it that a user who likes item A will like item B learn probabilities from user ratingspreferences

bull Others rule-based other data mining techniques


bull complete input data is required

bull pre-computation not possible

bull does not scale well bull high quality of

recommendations

bull abstraction (model) of input data

bull pre-computation (partially) possible (model has to be re-built from time to time)

bull scales betterbull abstraction may reduce

recommendation quality

Memory vs Model-based


bull collaborative filtering lsquoneighborhoodsrsquo of people with similar interest amp recommending items based on likings in neighborhood

bull limitations next to lsquocold startrsquo and lsquosparsityrsquo the lack of control (over onersquos neighborhood) is also a problem ie cannot add lsquotrustedrsquo people nor exclude lsquostrangersquo ones

bull therefore interest in lsquosocial recommendersrsquo where presence of social connections defines the similarity in interests (eg social tagging CiteULike)bull does a social connection indicate user interest similaritybull how much users interest similarity depends on the strength

of their connectionbull is it feasible to use a social network as a personalized

recommendation[Lin amp Brusilovsky Social Networks and Interest Similarity The Case of CiteULike HTrsquo10]

Social Networks amp Interest Similarity

bull unilaterally connected pairs have more common itemsmetadatatags than non-connected pairsbull highest similarity for direct connections - decreasing with the increase of distance between users in SN bull reciprocal relationship users - significantly larger similarity than users in a unidirectional relationship bull traditional item-level similarity may be less reliable to find similar users in social bookmarking systemsbull peers connected by self-defined social connections could be a useful source for cross-recommendation

Conclusions


bull Input characteristics of items amp interests of a user into characteristics of items =gt Recommend items that feature characteristics which meet the userrsquos interests

bull Techniquesbull Data mining methods Cluster items based on their

characteristics =gt Infer usersrsquo interests into clustersbull IR methods Represent items amp users as term vectors

=gt Compute similarity between user profile vector and items

bull Utility-based methods Utility function that gets an item as input the parameters of the utility function are customized via preferences of a user

Content-based Recommendations



Content Features


User Model


Recommendations

RecSys Issuesbull Cold-start problem (new user problem) nolittle data available to infer preferences of new

usersbull Changing User Preferences user interests may change over timebull Sparsity problem (new item problem) item descriptions are sparse eg not many user

rated or tagged an itembull Lack of Diversity (overfitting) when adapting too strongly to the preferences of users they

might see samesimilar recommendationsbull Use the right context users do things which might not be relevant for their user model

eg try out things do stuff for other peoplebull Research challenge right balance between serendipity amp personalizationbull Research challenge right way to use the influence of recommendations on userrsquos behavior


Announcementsbull Next deadlinesbull Tuesday March 1st 2359

Assignment 2bull Friday March 4th 1000 Post

questionbull Friday March 4th 1700 Vote

questions

image source httpwwwflickrcomphotosbionicteaching1375254387

Hands-on Teaser

bull Your Facebook Friendsrsquo popularity in a spread sheet

bull Locations of your Facebook Friendsbull Tag Cloud of your wall posts

Social Web 2016

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Announcements

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theory amp techniques for how to design amp evaluate

recommenders amp user models to use in Social Web applications





Kevin Kelly


User Modeling

















UM Basic Concepts












applications





Overlay User Models












User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

Slide 50




Kevin Kelly


User Modeling

















UM Basic Concepts












applications





Overlay User Models












User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

Slide 50
















UM Basic Concepts












applications





Overlay User Models












User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

Slide 50











applications





Overlay User Models












User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

Slide 50





Overlay User Models












User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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User Stereotypes




Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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Profile




















User Adaptation


experience
















personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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User Adaptation


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personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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personalisation















Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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Possible Metrics










March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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March 28 2013
















recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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recommendations














Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Conclusions










Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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Content Features


User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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User Model


Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

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Recommendations










questions


Hands-on Teaser



Social Web 2016

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Announcements

Slide 50










questions


Hands-on Teaser



Social Web 2016

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Announcements

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Hands-on Teaser



Social Web 2016

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Announcements

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vu university amsterdam - the social web 2016 - lecture 5

Education