[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommendation


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Context-aware recommendation (CARS) has been shown to be an effective approach to recommendation in a number of domains. However, the problem of identifying appropriate contextual variables remains: using too many contextual variables risks a drastic increase in dimensionality and a loss of accuracy in recommendation. In this paper, we propose a novel treatment of context – identifying influential contexts for different algorithm components instead of for the whole algorithm. Based on this idea, we take traditional user-based collaborative filtering (CF) as an example, decompose it into three context-sensitive components, and propose a hybrid contextual approach. We then identify appropriate relaxations of contextual constraints for each algorithm component. The effectiveness of context relaxation is demonstrated by comparison of three algorithms using a travel data set: a contenxt-ignorant approach, contextual pre-filtering, and our hybrid contextual algorithm. The experiments show that choosing an appropriate relaxation of the contextual constraints for each component of an algorithm outperforms strict application of the context.

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[ECWEB2012]Differential Context Relaxation for Context-Aware Travel Recommendation

  1. 1. Differential Context Relaxation for Context-Aware Travel RecomendationYong Zheng, Robin Burke, Bamshad MobasherCenter for Web IntelligenceDePaul University
  2. 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. 3. Normal Recommendation Profile Restaurant1 Rating1 Restaurant2 Rating2 ... ...
  4. 4. Context-AwareRecommendation Profile Restaurant1 Rating1 Context1 Restaurant2 Rating2 Context2
  5. 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. 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. 7. Relaxation etc.
  8. 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. 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. 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. 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   vN wv  (rv ,i  rv ) w vN v
  12. 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   vN wv  (rv,i  rv ) u w v vN
  13. 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   vN wv  (rv,i  rv ) u w v vN
  14. 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   vN wv  (rv,i  rv ) u w v vN
  15. 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. 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. 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. 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. 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. 20. Results
  21. 21. Optimal constraints
  22. 22. Sensitivity
  23. 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. 24. Questions