Differential Context Modeling in Collaborative FilteringYong Zheng, Robin Burke, Bamshad MobasherCenter for Web Intelligen...
Overview• Recommender Systems (RS) & Context-aware RS• Research Problem: Sparsity of Contexts• Differential Context Modeli...
Introduction• Recommender Systems• Context-aware Recommender Systems
Recommender Systems (RS)• Information Overload  Recommendations• ApplicationsE-commerce: Amazon, EBayMovie: Netflix, Mo...
Context-aware RS (CARS)• Traditional RS: Users × Items  Ratings• Contextual RS: Users × Items × Contexts RatingsCompanio...
Research Problem• Sparsity of Contexts• Relevant Solutions Context Matching (baseline) Context Selection Context Relaxa...
Sparsity of Contexts• An example in the movie domainAre there previous rating profiles within the same context? (Contexts ...
Relevant Solutions• Context Matching (baseline)  No contexts exactly matched!• Context Selections  Select a list of infl...
Differential Context Modeling• DCM Framework• Relevant Techniques Differential Context Relaxation (DCR) Differential Con...
Differential Context Modeling (DCM)• There are two parts in DCM “Differential” PartSeparate one algorithm into different ...
Differential Context Modeling (DCM)• Characteristics of DCM (i.e. DCR and DCW) A general framework  any algorithms with ...
DCM in Collaborative Filtering• Collaborative Filtering (CF) User-based CF Item-based CF Slope One Recommender• DCM in ...
Collaborative Filtering (CF)• CF is an efficient algorithm in traditional RS. Memory-based CFSuch as K-Nearest Neighbor (...
DCM in CF• DCR and DCW have been successfully evaluated on UBCF. Y. Zheng, R. Burke, B. Mobasher. "Differential Context R...
Workflow in DCM• There are two parts in DCM “Differential” Part  Algorithm DecompositionSeparate one algorithm into diff...
DCM in Item-based CF (IBCF)• User and Item-based Collaborative FilteringPirates of theCaribbean 4Kung Fu Panda 2 Harry Pot...
DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Relaxation (DCR)Put IBCF on DCR123
DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Weighting (DCW)One important notion: similarity of contex...
DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Weighting (DCW)Put IBCF on DCWσ is the weighting vector, ...
DCM in Slope One Recommender• DCR and DCWJust provide the formula here, due to limited presentation time.In DCRIn DCW
Optimizer - PSO• Particle Swarm intelligence (PSO)DCR  Feature Selection  Modeled by a Binary Vector;DCW  Feature Weigh...
Optimizer - PSO• Particle Swarm intelligence (PSO)Swarm = a group of birdsParticle = each bird ≈ each run in algorithmVect...
Experimental Results• Data Sets• Evaluations Predictive Performances Performance of Optimizers
Data Sets (from surveys)AIST Food Data Movie Data# of Ratings 6360 1010# of Users 212 69# of Items 20 176# of ContextsReal...
Evaluation ProtocolsWe measured predictions by root-mean-square error (RMSE) and coverage whichdenotes the percentage we c...
Predictive Performances (RMSE)DCW works the best, where pre-filtering is better than DCR but very low coverage!
Predictive Performances (RMSE)DCW works the best and DCR works better than two baselines!
Performance of OptimizersThree factors influencing the performance: 1). Density of Data; 2). DCW usuallyleaves more costs;...
It is gonna end….• Conclusions• Future Work
Last but not the leastWhat we did? – Expand DCM (i.e. DCR and DCW) to IBCF and Slope One recommender.Conclusions:As a gene...
ReferencesY. Zheng, R. Burke, B. Mobasher. "Differential Context Relaxation for Context-aware Travel Recommendation". InE...
Thank You !Center for Web Intelligence, DePaul University, Chicago, IL USAWe are leaving the age of information, and enter...
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[SOCRS2013]Differential Context Modeling in Collaborative Filtering

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Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.

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[SOCRS2013]Differential Context Modeling in Collaborative Filtering

  1. 1. Differential Context Modeling in Collaborative FilteringYong Zheng, Robin Burke, Bamshad MobasherCenter for Web Intelligence, DePaul UniversitySOCRS-2013, DePaul UniversityMay 31, 2013
  2. 2. Overview• Recommender Systems (RS) & Context-aware RS• Research Problem: Sparsity of Contexts• Differential Context Modeling (DCM) Framework• DCM in Collaborative Filtering• Experimental Analysis• Conclusions and Future Work
  3. 3. Introduction• Recommender Systems• Context-aware Recommender Systems
  4. 4. Recommender Systems (RS)• Information Overload  Recommendations• ApplicationsE-commerce: Amazon, EBayMovie: Netflix, MoviePilotMusic: Pandora, Last.fmSocial: Facebook, twitter, etc
  5. 5. Context-aware RS (CARS)• Traditional RS: Users × Items  Ratings• Contextual RS: Users × Items × Contexts RatingsCompanionRecommendation cannot live alone without considering contexts.
  6. 6. Research Problem• Sparsity of Contexts• Relevant Solutions Context Matching (baseline) Context Selection Context Relaxation Context Weighting
  7. 7. Sparsity of Contexts• An example in the movie domainAre there previous rating profiles within the same context? (Contexts Matching)User Item Time Location Companion RatingU1 T Weekend Home Girlfriend 4U2 T Weekday Home Girlfriend 5U3 T Weekday Cinema Sister 4U1 T Weekday Home Sister ?
  8. 8. Relevant Solutions• Context Matching (baseline)  No contexts exactly matched!• Context Selections  Select a list of influential ones, such as time & location.• Context Relaxation  Use a relaxed version, such as <weekday, home>• Context Weighting  Use all contexts, but measure how similar contexts are.User Item Time Location Companion RatingU1 T Weekend Home Girlfriend 4U2 T Weekday Home Girlfriend 5U3 T Weekday Cinema Sister 4U1 T Weekday Home Sister ?
  9. 9. Differential Context Modeling• DCM Framework• Relevant Techniques Differential Context Relaxation (DCR) Differential Context Weighting (DCW)
  10. 10. Differential Context Modeling (DCM)• There are two parts in DCM “Differential” PartSeparate one algorithm into different functional components;Apply differential context constraints to each component;To maximize the global contextual effects by algorithm components; “Modeling” PartIt can be performed by context relaxation or context weightingDifferential Context Relaxation (DCR)Differential Context Weighting (DCW)
  11. 11. Differential Context Modeling (DCM)• Characteristics of DCM (i.e. DCR and DCW) A general framework  any algorithms with multiple components An efficient framework  demonstrated to improve prediction accuracy In collaborative filtering, DCM is a general super set covered all previouswork in this area – previous work either uses just one component, or appliedthe same contextual constraints for all components.
  12. 12. DCM in Collaborative Filtering• Collaborative Filtering (CF) User-based CF Item-based CF Slope One Recommender• DCM in CF Algorithm Decomposition Application of DCR & DCW to CF
  13. 13. Collaborative Filtering (CF)• CF is an efficient algorithm in traditional RS. Memory-based CFSuch as K-Nearest Neighbor (KNN) based CF  Our attention right now! User-based collaborative filtering (UBCF); Item-based collaborative filtering (IBCF); Slope One Recommender; Model-based CF  Future WorkSuch as SVD, Matrix Factorization, etc Hybrid CF
  14. 14. DCM in CF• DCR and DCW have been successfully evaluated on UBCF. Y. Zheng, R. Burke, B. Mobasher. "Differential Context Relaxation for Context-aware Travel Recommendation".In Conference on EC-WEB, 2012 [DCR] Y. Zheng, R. Burke, B. Mobasher. "Optimal Feature Selection for Context-Aware Recommendation usingDifferential Relaxation". In ACM RecSys Workshop on CARS, 2012 [DCR + Optimizer] Y. Zheng, R. Burke, B. Mobasher. "Recommendation with Differential Context Weighting". In Conference onUMAP, 2013 [DCW]• Our work in SOCRS-2013: Extend DCM to other CF algorithms: Item-based collaborative filtering (IBCF) Slope One Recommender
  15. 15. Workflow in DCM• There are two parts in DCM “Differential” Part  Algorithm DecompositionSeparate one algorithm into different functional components;Apply differential context constraints to each component;To maximize the global contextual effects by algorithm components; “Modeling” Part  Context Relaxation or Context WeightingIt can be performed by context relaxation or context weightingDifferential Context Relaxation (DCR)Differential Context Weighting (DCW)
  16. 16. DCM in Item-based CF (IBCF)• User and Item-based Collaborative FilteringPirates of theCaribbean 4Kung Fu Panda 2 Harry Potter 6 Harry Potter 7U1 4 4 1 2U2 3 4 2 1U3 2 2 4 4U4 4 4 1 ?
  17. 17. DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Relaxation (DCR)Put IBCF on DCR123
  18. 18. DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Weighting (DCW)One important notion: similarity of contexts measured by Weighted Jaccard SimilarityUser Item Time Location Companion RatingU1 T Weekend Home Girlfriend 4U2 T Weekday Home Girlfriend 5U3 T Weekday Cinema Sister 4U1 T Weekday Home Sister ?σ is the weighting vector <w1, w2, w3> for three dimensions.Assume they are equal weights, w1 = w2 = w3 = 1.J(c, d, σ) = # of matched dimensions / # of all dimensions = 2/3
  19. 19. DCM in Item-based CF (IBCF)• Algorithm Decomposition and Context Weighting (DCW)Put IBCF on DCWσ is the weighting vector, and ϵ is a threshold for the similarity of contexts.i.e., only records with similar enough (≥ ϵ) can be included in the calculations
  20. 20. DCM in Slope One Recommender• DCR and DCWJust provide the formula here, due to limited presentation time.In DCRIn DCW
  21. 21. Optimizer - PSO• Particle Swarm intelligence (PSO)DCR  Feature Selection  Modeled by a Binary Vector;DCW  Feature Weighting  Modeled by a Real-number Vector;We need to find the optimal vectors in DCR and DCW.Binary PSO and PSO are solutions for DCR and DCW respectively.They have been successfully evaluated in our previous work.
  22. 22. Optimizer - PSO• Particle Swarm intelligence (PSO)Swarm = a group of birdsParticle = each bird ≈ each run in algorithmVector = bird’s position in the search space ≈ Vectors we needGoal = the location of pizza ≈ RMSESo, how to find goal by swam?1.Looking for the pizza, a machine can tell the distance2.Each iteration is an attempt or move3.Cognitive learning from particle itselfAm I closer to the pizza comparing withmy “best ”locations in previous history?4.Social Learning from the swarmHey, my distance is 1 mile. It is the closest! Follow me!!
  23. 23. Experimental Results• Data Sets• Evaluations Predictive Performances Performance of Optimizers
  24. 24. Data Sets (from surveys)AIST Food Data Movie Data# of Ratings 6360 1010# of Users 212 69# of Items 20 176# of ContextsReal hunger(full/normal/hungry)Virtual hungerTime (weekend, weekday)Location (home, cinema)Companions (friends, alone, etc)OtherFeaturesUser genderFood genre, Food styleFood stuffUser genderYear of the movieDensity Dense Sparse
  25. 25. Evaluation ProtocolsWe measured predictions by root-mean-square error (RMSE) and coverage whichdenotes the percentage we can find neighbors for a prediction.Our goal: improve RMSE (i.e. less errors) within a decent coverage.We allow a decline in coverage, because applying contextual constraints usuallybring low coverage (i.e. the sparsity of contexts!).Baselines: context-free CF, i.e. the original IBCF and slope one recommender contextual pre-filtering CF which just apply the contextual constraints to theneighbor selection component – no other components like DCM.
  26. 26. Predictive Performances (RMSE)DCW works the best, where pre-filtering is better than DCR but very low coverage!
  27. 27. Predictive Performances (RMSE)DCW works the best and DCR works better than two baselines!
  28. 28. Performance of OptimizersThree factors influencing the performance: 1). Density of Data; 2). DCW usuallyleaves more costs; 3).Which algorithm is used – Slope one is a memory cost one!
  29. 29. It is gonna end….• Conclusions• Future Work
  30. 30. Last but not the leastWhat we did? – Expand DCM (i.e. DCR and DCW) to IBCF and Slope One recommender.Conclusions:As a general framework, DCM can be applied to more algs other than UBCF!In terms of performances, DCW works the best, it compensates drawbacks in DCR.In term of the optimizer, we find three factors influencing the running performance.Future Work:1). Expand DCM to more algs? Such as latent factor models to make it more general;2). Evaluate it on larger data set and put DCM on MapReduce to speed up it!3). Try other similarity of contexts other than the simple weighted Jaccard similarity
  31. 31. ReferencesY. Zheng, R. Burke, B. Mobasher. "Differential Context Relaxation for Context-aware Travel Recommendation". InEC-WEB, 2012 [DCR]Y. Zheng, R. Burke, B. Mobasher. "Optimal Feature Selection for Context-Aware Recommendation usingDifferential Relaxation". In ACM RecSys Workshop on CARS, 2012 [DCR + Optimizer]Y. Zheng, R. Burke, B. Mobasher. "Recommendation with Differential Context Weighting". In UMAP, 2013 [DCW]Y. Zheng, R. Burke, B. Mobasher. “Differential Context Modeling in Collaborative Filtering". In SOCRS-2013,DePaul University, Chicago, IL, May 31, 2013 [DCM]
  32. 32. Thank You !Center for Web Intelligence, DePaul University, Chicago, IL USAWe are leaving the age of information, and entering the age of recommendation.

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