1. Technische Universität München 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. Technische Universität München 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 1.Recommender systems basics, design of our approach 2.Contextualization, example scenario: mobile tourist guide 2/12

3. Technische Universität München 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 3/12

4. Technische Universität München 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 4/12

5. Technische Universität München 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, rate and recommend images on PDA –Windows Mobile (.NET Compact Framework) Tested in small user study (13 users) 5/12

6. Technische Universität München Scenario with Public Shared Display 6/12

7. Technische Universität München Contexualization So far, mobile recommender on PDA, but not context-aware Goal: Adapt to the current user situation (time, position, …) Proposed method is a combination score, e.g. linear: –score = a * cf-score + b * ctx-score scores: +1 best value; -1 worst value cf-score: rating prediction according to the explained item- based collaborative filtering ctx-score: score according to the current user context –For example, current distance to point-of-interest (POI) –ctx-score = -1 meaning, for example POI is too far away Restaurant is currently closed 7/12

8. Technische Universität München Scenarios Mobile exhibition guide –Search for products, exhibitors or places of interest etc., additional functions such as appointment schedule, virtual business cards –Context: Location of exhibition booths and halls Indoor positioning with bluetooth-based infrastructure Mobile city guide for tourists –So far: application on PDA displays information (image/text) and play audio file for nearest POI (sight) Extended with explained decentral CF method –Devices are not networked, model is updated when tourists return device Context: POIs in vicinity, ranked according to CF score –Rating acquisition Explicit: User enters rating (good/mediocre/bad, or 5 star scale) Implicit: System determine rating by usage of audio file –Implemented and tested 8/12

9. Technische Universität München User Interface Mobile City Guide 9/12

10. Technische Universität München 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 agree 7% 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 10/12

11. Technische Universität München Conclusion Summary –Implementing a decentralized recommender system for PDAs Innovations include storage optimization, improved extensibility and group recommendations –Contextualized application in a mobile tourist guide User study with early promising results Current & future work –Mobile tourist guide, ongoing study w.r.t. recommendation quality Are recommended POIs really more interesting for users than just nearest POIs? Are than any differences between recommendations based on implicit versus explicit ratings? –Improve context-awareness, refine method to combine CF and context scores –Test in other scenario(s) 11/12

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