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[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Factorizing Contexts

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Improve General Contextual SLIM Recommendation Algorithms By Factorizing Contexts

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[SAC 2015] Improve General Contextual SLIM Recommendation Algorithms By Factorizing Contexts

  1. 1. Improve General Contextual SLIM Recommendation Algorithms By Factorizing Contexts Yong Zheng, PhDc Center for Web Intelligence DePaul University, Chicago, USA Student Research Competition 2nd Round@ACM SAC 2015 Salamanca, Spain, April 15
  2. 2. Overview • Background (Recommender System and Context-aware RS) • Research Problems and Motivations • Solutions by Factorizing Context • Experimental Results • Conclusion and Future Work
  3. 3. Background • Recommender Systems • Context-aware Recommender Systems
  4. 4. Intro. Recommender Systems • Recommender System (RS) Recommendation
  5. 5. Intro. Recommender Systems • Recommender System (RS) Social RS (Twitter) Tagging RS (Flickr)
  6. 6. Intro. Recommender Systems • Recommender System (RS)
  7. 7. Intro. Recommender Systems • Typical Data Set in RS: Rating-Based Data Set Usually, it is a 2D rating matrix: User × Item -> Ratings Task: For a user, RS provide a list of suggested items to him/her
  8. 8. Context-aware RS (CARS) • Example of Context-aware Recommender What is context? “any information that can be used to characterize the situation of an entity” by Abowd et al. in 1999 Companion Example of Contexts in different domains:  Food: time (noon, night), occasion (business lunch, family dinner)  Movie: time (weekend, weekday), location (home, cinema), etc  Music: time (morning, evening), activity (study, sports, party), etc  Book: a book as a gift for kids or mother, etc
  9. 9. Context-aware RS (CARS) • Traditional RS: Users × Items  Ratings • Context-aware RS: Users × Items × Contexts Ratings Task: CARS provide a list of suggests to <user, contexts> Recommendation cannot live alone without considering contexts. E.g., choose a romantic movie with partner, but comic with kids.
  10. 10. Research Problems • Contribution of This Work • Research Problem and Motivations
  11. 11. Contribution of This Work Before moving on, it is necessary to introduce the contribution of this work: Basically, the work in this paper is an improvement over a context-aware recommendation algorithm which was our previous work published in ACM CIKM 2014 and ACM RecSys 2014 conferences. To further introduce the following work: 1). Introduce the General Contextual Sparse LInear Method (GCSLIM) published in ACM CIKM 2014 2). Introduce the drawbacks in GCSLIM and research problems in this work
  12. 12. Intro. GCSLIM General Contextual Sparse LInear Method (GCSLIM) published in ACM CIKM 2014 Recommendation task: given user U1, and a context situation {weekday, home, sister}, the system should provide a list of recommended movies to U1. How to generate such a recommendation list? User Movie Time Location Companion Rating U1 M1 Weekend Home Girlfriend 4 U2 M2 Weekday Home Girlfriend 5 U3 M3 Weekday Cinema Sister 4 U1 M2 Weekday Home Sister N/A
  13. 13. Intro. GCSLIM Task: Recommendation to <U1, Ctx2> Step1: we extract U1’s contextual rating on other movies from P, e.g., R (U1, t1, Ctx1) Step 2: This rating will be converted into R (U1, t1, Ctx2) by adding the rating deviation Dev(Ctx1, Ctx2) Step 3: Right now, we have estimated rating R (U1, t1, Ctx2) , we multiply it with coefficient W (t1, t2) S(U1, t2, Ctx2) = an aggregation of the term below: [R(U1, t1, Ctx1) + Dev (Ctx1, Ctx2)] × W(t1, t2)
  14. 14. Intro. GCSLIM S(U1, t2, Ctx2) = an aggregation of the term below: [R(U1, t1, Ctx1) + Dev (Ctx1, Ctx2)] × W(t1, t2) Ctx1 = {weekend, home, girlfriend} Ctx2 = {weekday, home, sister} Dev (Ctx1, Ctx2) = Dev (weekend, weekday) + Dev (girlfriend, sister) User Movie Time Location Companion Rating U1 M1 Weekend Home Girlfriend 4 U2 M2 Weekday Home Girlfriend 5 U3 M3 Weekday Cinema Sister 4 U1 M2 Weekday Home Sister N/A
  15. 15. Intro. GCSLIM How good is the GCSLIM????????? In our previous work published in ACM CIKM 2014, it has been demonstrated that GCSLIM outperforms the state-of-the-art context-aware recommendation algorithms within multiple data sets. In other words, GCSLIM is the BEST context-aware recommendation algorithm based on our empirical experimental evaluations in 2014.
  16. 16. Drawbacks in GCSLIM Ctx1 = {weekend, home, girlfriend} Ctx2 = {weekday, home, sister} Dev (Ctx1, Ctx2) = Dev (weekend, weekday) + Dev (girlfriend, sister) -------------------------------------------------------- Deviations is measured in pairs, e.g. Dev (weekend, weekday) , which may result in sparsity problem. For example: In training set, we learned Dev<weekend, weekday> and Dev <weekday, holiday> But in testing set, we need Dev <weekend, holiday> Which was NOT learned in the algorithm
  17. 17. Research Problem Problem: how to alleviate the sparsity problem? Sparsity Problem: In training set, we learned Dev<weekend, weekday> and Dev <weekday, holiday> But in testing set, we need Dev <weekend, holiday> Which was NOT learned in the algorithm
  18. 18. Solutions and Experimental Results • Solution: Factorizing Context • Experimental Results
  19. 19. Solution Problem: In training set, we learned Dev<weekend, weekday> and Dev <weekday, holiday> But in testing set, we need Dev <weekend, holiday> Which was NOT learned in the algorithm ---------------------------------------------------------------- Solution: We represent each context condition by a vector, e.g., Weekend = <0.1, 0, 0.03, 0.4, 1> Weekday = <0.2, 0.3, 1.2, 0, 0.1> Holiday = <1.22, 0.1, 0, 0.2, 2.3> Dev (c1, c2) = Euclidean distance (Vector1, Vector2) Note: other distance metrics may also be applied!!
  20. 20. Solution Problem: In training set, we learned Dev<weekend, weekday> and Dev <weekday, holiday> But in testing set, we need Dev <weekend, holiday> Which was NOT learned in the algorithm ------------------------------------------------------------------ Solution: Training: Dev<weekend, weekday> Dev <weekday, holiday> Testing: Dev<weekend, holiday> In training, we already learned those two pairs of deviations, where their corresponding vectors have been learned! In testing, we can directly use the distance of the vectors “weekend” and “holiday” to compute the deviations!!  Alleviate the sparsity!!
  21. 21. Experimental Results This approach has been evaluated over multiple data sets: Due to limited space in the SRC paper, we just present our results based on the restaurant data, where there are two contextual dimensions: Time (weekend, weekday), Location (school, home, work) We use two metrics: Precision, and Mean Average Precision (MAP) Precision  measure how accurate the recommendation list is by evaluating the hit-ratio MAP  additionally measure the ranking positions in addition to Precision
  22. 22. Experimental Results We used two baselines from our previous work published in RecSys and CIKM 2014. We did not include other baselines, since GCSLIM was already proved as the best one in our previous work. The improvement is 16% on precision and 8% on MAP for this data set.
  23. 23. It is gonna end… • Conclusions • Future Work
  24. 24. Conclusions  Factorizing context is able to alleviate the sparsity problem in the GCSLIM algorithm Future Work  Even if this solution can alleviate the sparsity problem, but it cannot fully solve it when the data is too sparse, especially when it comes to the cold-start problems: no knowledge about user/item/context. Stay tuned ..  Actually, the idea of factorizing context is also applicable to other algorithms to alleviate the sparsity problem, since this problem is a general one in this domain.  We have a paper "Integrating Context Similarity with Sparse Linear Recommendation Model" accepted by the UMAP 2015 which is the premier conference in user modeling and personalization, where we reused the approach of factorizing context.
  25. 25. Acknowledgement Thanks to the ACM SIGAPP providing travel support; Thanks to Microsoft Research providing travel support; Thanks to the organizers of ACM SAC and the SRC program!
  26. 26. Thank You! Center for Web Intelligence, DePaul University, Chicago, IL USA

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