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- 1. Differential Context Relaxation for Context-Aware Travel RecomendationYong Zheng, Robin Burke, Bamshad MobasherCenter for Web IntelligenceDePaul University
- 2. Recommender System Any system that guides the user in a personalized way to interesting or useful objects in a large space of possible options or that produces such objects as output. Context-aware recommendation means that our definition of “useful” includes contextual considerations
- 3. Normal Recommendation Profile Restaurant1 Rating1 Restaurant2 Rating2 ... ...
- 4. Context-AwareRecommendation Profile Restaurant1 Rating1 Context1 Restaurant2 Rating2 Context2
- 5. Approaches to CARS Filtering discard all options not appropriate to context (either before or after) Modeling build context into recommendation model Critical question What contextual features matter? The more context features we use The more options are filtered out The sparser the modeling space
- 6. Context as constraints We can view context-aware recommendation as imposing additional constraints on recommendation Options must be suitable to the context But we may be willing to relax these constraints to find nearby options
- 7. Relaxation etc.
- 8. Context matching Assume we have a set of contextual features c c = < f1, f2, f3, ... fn > Define a set of constraints, C Two contexts c and d match relative to constraints C iff Each feature in c and d matches relative to the corresponding constraint in C
- 9. Example: Hotel Ratings { trip type, days stayed, origin city, destination city, month of departure } c1 = {business, 3, Los Angeles, Chicago, July} c2 = {business, 7, Seattle, Chicago, January} Should they match or not?
- 10. Matching with constraints Two contexts c1 = {business, 3, Los Angeles, Chicago, July} c2 = {business, 7, Seattle, Chicago, January} Cstrict = { (exact trip type), (exact duration), (exact origin), (exact destination), (exact month) } no match Crelaxed = { (exact trip type), (any duration), (contained time_zone origin), (exact destination), (any month) } now the two contexts match If we are predicting for a user in context c2, we would not use a rating with context c1, if we apply constraint Cstrict we would use it, if we apply Crelaxed
- 11. Differential Context Relaxation The idea is to apply context to different components of a recommendation algorithm Rather than applying it in a uniform way Example kNN collaborative recommendation via Resnick’s algorithm Pred(u , i ) ru vN wv (rv ,i rv ) w vN v
- 12. Component 1: Neighbors Original algorithm Select neighbors who have rated item i Context-aware given context c Select neighbors who have rated item i in context matching c, relative to constraint C1 Pred(u, i, c) r vN wv (rv,i rv ) u w v vN
- 13. Component 2: Peer Baseline Original algorithm Average over all of the ratings by a neighbor to establish a baseline Context-aware given context C Average over only those ratings matching c given constraint C2 Pred(u, i, c) r vN wv (rv,i rv ) u w v vN
- 14. Component 3: User baseline Original algorithm Average over all of the target user’s ratings to establish a baseline Context-aware given context C Average over the target users ratings given in contexts that match c, relative to constraint C3 Pred(u, i, c) r vN wv (rv,i rv ) u w v vN
- 15. Question How to choose C1, C2, C3 to make best use of the context information In other words what is the optimum relaxation of the contextual constraint applied differentially to each algorithm component?
- 16. Data set Tripadvisor Top 50 US cities 2,562 users 1,455 hotels 9,251 ratings Fairly difficult recommendation task some work using “Trip Type” as a contextual variable
- 17. Context-linked features We decided to use user location and hotel location as context features Strictly speaking demographic content However research in the travel domain (Klenosky and Gitelson, 1998) shows these factors influence user’s expectations different standards for a California hotel vs a Nevada one behave like contextual features We call these “context-linked” features
- 18. Feature space trip type – solo, family, business, etc. origin city contained state contained time zone destination city contained state contained time zone Total of 32 possibilities
- 19. Optimization 32 feature possibilities 3 components 323 = 32k possible constraint combinations But possible to eliminate some possibilities Example when averaging over a given user’s ratings user location is irrelevant will not filter anything out Able to shrink to < 400 combinations enough for exhaustive search
- 20. Results
- 21. Optimal constraints
- 22. Sensitivity
- 23. Differential ContextRelaxation Lets us incorporate context While managing the tradeoff between accuracy and coverage Future considerations other algorithms F1 optimization constraint instead of binary matching, real-valued? instead of selection, weighting of features? scalable optimization Stay tuned! RecSys CARS workshop Yong Zheng, Robin Burke, Bamshad Mobasher. "Optimal Feature Selection for Context-Aware Recommendation using Differential Relaxation". Proceedings of the 4th International Workshop on Context-Aware Recommender Systems (CARS 2012) held in conjunction with the 6th ACM Conference on Recommender Systems (RecSys 2012), Dublin, Ireland, Sep 2012
- 24. Questions

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