1. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20061 Research Projects in the Area of Context Awareness University of Zurich Seminar Context Aware Computing SS 2006 Sinja Helfenstein Edoardo Beutler

2. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20062 Agenda Context Aware Recommender SystemsSinja COMPASS (COntext-aware Mobile Personal ASSistant) UbiMate Conclusion and Outlook Recognizing the Places We GoEdoardo BeaconPrint Conclusion

3. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20063 Recommender Systems and Context Aware Systems Recommender Systems Categorization & Recommendations based on interests / user profile TripAdvisor.com FindAndDine.ch Context Aware Systems Categorization & Recommendations based on user's current situation (context) GPS Navigation Systems Location Based Services Provision of information relevant to the user – in consideration of its context and interests Static Environment! Uniform Interests! Goal: Provision of information relevant to the user Information Selection Criteria: Hard: Filtering of useless items Soft:Rating of potentially useful items

4. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20064 COMPASS Context-aware Mobile Personal Assistant Ratings Buddie s Interactivity: - Reservations - Calling buddies Display as list or in map Location-specific information for tourists: Buildings, Restaurants, Hotels, Buddies, Taxi stands, Landmarks, etc.

5. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20065 COMPASS: Recommendation Criteria & Strategy Recommendation Criteria: Hard:Current Location Subscriptions Application Specifics Soft:Users Interests Multiple Prediction Strategies possible, manual selection per item-group. One Soft Criteria only  One average rating per item  Useful for Real World-information in a dynamic environment? Example: Restaurant Guide Bar Rimini: clear sky, 35° = rainy, 10°? bqm: with friends = with grandma? Company Open. hrs Weather

6. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20066 Collaborative Filtering (CF) Classical Collaborative Filtering 1) Compare users by their individual ratings and define neighbours (Pearson correlation coefficient) 2) Prediction for User u, based on neighbours' ratings (weighted by correlation) Best-known example:

7. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20067 Introducing Context-Awareness to CF Association of various context-values to each rating Ratings' relevance for current prediction depending on: user AND context similarity! Implicit classification by context information in user feedback. Advantages over classical CF-Systems: Multiple ratings per item if used in different context. Implicit feedback possible by inferring rating from user behaviour (e.g. duration of stay, frequency of visits).

8. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20068 [3] Mobile city guide based on context-aware collaborative filtering Method: Look at what like-minded user have done in the past under similar context to predict what the current user may like to do Context used:Information SourceHard/Soft –User InformationManually defined user profileS –Social EnvironmentManually Entered by UserS –Tasks (Activity)Manually Selected by User*H –LocationGPS-ModuleH –Infrastructure- –Physical Conditions WeatherOnline Content ProviderS TimeMobile DeviceS *Currently available activities: –Food (Restaurants, Bars, Take Away, etc.) –Entertainment (Sport, Culture, Spa, etc.) –Shopping (Groceries, Fashion, Art, etc.)

9. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 20069 UbiMate : Demonstration Testversion online: http://ubimate.hopto.org For Site Access: Username:friends Password: ubiubi Personal registration needed for participation

10. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200610 Conclusion High potential for context awareness in mobile recommender systems –Implicit feedback increases amount of ratings and data quality –CF for handling the information flood resulting from multiple context dimensions (the more dimensions the better the prediction) Outlook –Improve context recognition and inference –Improve usability –Improve interactivity

11. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200611 BeaconPrint for Recognition of Places We Go End goal: Define important places with names, not just coordinates. Technique: WiFi and GMS, but not GPS (skyscraper-canyons) BeaconPrint... does: Provides a possibility to „extract“ relevant places from raw data. “The mechanism for learning the physical destinations in someone's life and detect whenever their devices return to those places“ does not: Assign automatically names or semantics to a place. (geocoding)

12. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200612 Algorithmic Tasks Learning algorithm 1. Segment assign a waypoint whenever the device is in a stable place. 2. Merge waypoints from repeat visits. Recognition algorithm 1. Recognize a device returning to a known place. 2. Recognize a device not in a place (mobile state). → BeaconPrint is a learning and recognition algorithm.

13. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200613 Related Work Ashbrook and Starner's GPS Dropout plus Hierarchical Clustering Algorithm Marking positions where for at least t minutes no GPS signal is received or the speed is below 1 mile per hour. The comMotion Recurring GPS Dropout Algorithm A position where the GPS signal is lost at least three times within a given radius is marked as important place. Kang et al.'s Sensor-Agnostic Temporal Point Clustering Algorithm Avoids the high dependence of a proper GPS signal by using temporal point clustering.

14. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200614 BeaconPrint Algorithm Gathering continually statistics about the radio environment. Parameters: Time window w - stable scans for at least w indicate a signifificant place. Certainty parameter c in [0... c max ] and d = w/c max - no new beacon for d time indicates a stale scan. Not signal strength is the fingerprint metric, but constructs its fingerprint using a response-rate histogram (1-beacon loss rate responsrate).

15. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200615 Conclusion Runs on common hardware (WiFi, GSM) Recognizes and learns places to over 90% accurate. People have 72.3 places they go, only 1-2 frequent and 7-8 once a week. Former algorithms recognized only 5-35% of infrequent places (visited once for <10 min), BeaconPrint over 63%. In the second visit, the accuracy is increased to 80%.

16. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200616 Conclusion

17. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200617 Conclusion

18. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200618 References [1]A. K. Dey, G. D. Abowd: Towards a Better Understanding of Context and Context- Awareness [2]M. Van Stetten, S. Pokraev, J. Koolwaaji: Context-Aware Recommendations in the Mobile Tourist Application COMPASS [3]A. Chen: Context-Aware Collaborative Filtering System: Predicting the User's Preferences in Ubiquitous Computing [4]A. Schmidt, M. Beigl, H-W. Gellersen: There is more to context than location [5] J. Hightower, S. Consolvo, A. LaMarca, I. Smith, J. Hughes: Learning and Recognizing the Places We Go

19. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200619 UbiMate :Context Modeling [3] Snapshot of Context = Composite of different types of context data from various sources. –Manually entered by user –Direct sensory input (integrated in device / external) –Derived (e.g. using location to fetch weather from content provider) Supporting hierarchies without redundancy –Context objects maintain their values and related hierarchy –Rating objects are associated with the specific context values inside context objects Flexibility –Easy adding of new context types –Handling of missing context information Backup

20. Context Awareness – Edoardo Beutler & Sinja Helfenstein Monday, 22 May 200620 UbiMate : Associating context with ratings Backup