Wolfgang Woerndl , Henrik Muehe, Stefan Rothlehner, Korbinian Moegele

1. Context-Aware Recommendations in Decentralized, Item-Based Collaborative Filtering on Mobile Devices Wolfgang Woerndl, Henrik Muehe,Stefan Rothlehner, Korbinian Moegele Technische Universitaet Muenchen Munich, Germany

2. Motivation • Recommender systems • Successful application for example in online shops • As yet mostly centralized systems • Mobile scenario promising, more focused information access • Decentralized, mobile recommender systems • Recommendation on client, connection to server not required • Potential advantages with regard to privacy • Goal of this work • Design, implementation and test of a decentralized recommender system for Windows Mobile PDAs + context-aware application • Recommender systems basics, design of our approach • Contextualization, example scenario: mobile tourist guide

3. Recommender Systems • Recommender systems • Individual approaches  Consider only active user • Collaborative filtering  Consider also ratings of other users • Collaborative Filtering (CF) • User-based CF • Determine similar users  Drawbacks include cold start, performance • Item-based (or model-based) collaborative filtering • Calculate model of pairwise item similarities based on ratings  Advantage: can be pre-computed in advance • Recommend items that are similar to items that have been positively rated by the active user in the past

4. Decentralized, Mobile Recommender System • Decentralized approach • Peers in system exchange rating vectors of their users • Each peer computes local matrix of item-item similarities • Recommendation based on rating vector of user • PocketLens • Decentralized approach from research literature • Stores intermediate results when calculating item similarity • Disadvantages • Very big data model (item-item similarity) • Limited extensibility, only new rating vectors • Our system implements decentral, item-based collaborative filtering for PDAs

5. Our approach • Optimization of storage requirements • Extensibility of model by introducing versioned rating vectors • Store history of ratings • Allow for changing and deleting ratings • Integration of group recommendations • Users in front of shared public display • Implemented scenario: display, rateand recommend images on PDA • Windows Mobile (.NET Compact Framework) • Tested in small user study (13 users)

6. Scenario with Public Shared Display

7. Contexualization • So far, mobilerecommender on PDA, but not context-aware • Goal: Adapttothecurrentusersituation (time, position, …) • Proposedmethodis a combination score, e.g. linear: • score = a * cf-score + b * ctx-score • scores: +1 best value; -1 worstvalue • cf-score: ratingpredictionaccordingtotheexplained item-basedcollaborativefiltering • ctx-score: score accordingtothecurrentusercontext • Forexample, currentdistancetopoint-of-interest (POI) • ctx-score = -1 meaning, forexample • POI istoo far away • Restaurantiscurrentlyclosed

8. Scenarios • Mobile exhibitionguide • Search for products, exhibitors or places of interest etc., additional functions such as appointment schedule, virtual business cards • Context: Locationofexhibitionboothsandhalls • Indoorpositioningwithbluetooth-basedinfrastructure • Mobile cityguidefortourists • So far: application on PDA displaysinformation (image/text) andplayaudiofilefornearest POI (sight) • Extended withexplaineddecentral CF method • Devices are not networked, model isupdatedwhentouristsreturndevice • Context: POIs in vicinity, rankedaccordingto CF score • Rating acquisition • Explicit: User entersrating (good/mediocre/bad, or 5 starscale) • Implicit: System determineratingbyusageofaudiofile • Implementedandtested

9. User Interface Mobile City Guide

10. Evaluation • User study with real users (tourists) in Prague • 2 weeks in late september, 30 volunteering participants, aged between 17 and 76, various nationalities • Users could use city guide with recommender system for free and fill out questionnaire, instead of paying for rental • Questionnaire with 10 questions • Positive feedback, users liked application and recommender system • Example question: „I felt that the mobile guide selected sights according to my interests/ratings“ • 17% totally agree, 36% agree, 23% tend to agree7% tend to disagree, 10% disagree, 7% totally disagree • User are happy to give away some information about themselves in return for personalized recommendations • But users gave mixed feedback regarding explicit rating dialogue • Rely more on implicit ratings in future

11. Conclusion • Summary • Implementing a decentralizedrecommendersystemfor PDAs • Innovationsincludestorageoptimization, improvedextensibilityandgrouprecommendations • Contextualizedapplication in a mobile touristguide • User studywithearly promising results • Current & futurework • Mobile touristguide, ongoingstudyw.r.t. recommendationquality • Are recommended POIs reallymoreinterestingforusersthan just nearest POIs? • Are thananydifferencesbetweenrecommendationsbased onimplicit versus explicit ratings? • Improvecontext-awareness, refinemethodtocombine CF andcontextscores • Test in otherscenario(s)

12. Context-Aware Recommendations in Decentralized, Item-Based Collaborative Filtering on Mobile Devices Wolfgang Woerndl woerndl@in.tum.de Questions?