Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks
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Upload date: 2019-10-11 14:15:09


# In short CIKM 2015 article, one of the core reference on HIN-based recommendation, they consider a weighted variation which allows them to deal with rated cases. # Summary ### 1. Introduction ##### context and problem - more and more research on HIN for data mining: similarity search, clustering, classification - recently on reco: 2 (Jamali et al), 7 (Luo et al), 15 (Sun et al): interesting because possibility to integrate various kind of information (cf Fig 1), ex: user-movie-user is more or less a CF model - pb1: no values on links while recommendations are often associated with ratings (ex: Tom and Mary on Fig1, who have seen different movies but very different ratings) => necessary to generalize the formalism of HIN in order to account for the link weights - pb2: problem to combine information from different meta-paths, a good method of weight learning should allow to obtain personalized weights, weights should contribute to explanation, but if we personalize recommendation data sparsity problems get worst - contributions: extend HINs to weighted case ; propose SemRec (semantic path based RS) which flexibly include information of heterogeneous nature ; define consistency rule of similar users to circumvent data sparsity pb ; empirically study 2 datasets: Yelp and Douban ### 2. Heterogeneous network framework for recommendation ##### 2.1 Basic concepts - HIN for the weighted case (as usual, but with weights on one or more relation) ; illustration from Fig 2a - extended meta-paths to paths with attributes: links weights must be in a given range (give illustration) ##### 2.2 Recommendation on HIN - Table1: semantic example associated to meta-paths - Discussion about how different RS models will use meta-paths ##### 2.3 Similarity measure based on weighted meta-path - go through literature reco models based on paths in HIN (but no WHIN): PathSim (12), PCRW (4), HeteSim(10) ; cannot be simply transposed as they have no notion of weight - they adapt to this context with "atomic meta path": meta-path where weights take specific values - illustration on Fig3 on the score decomposition in the same fashion as PathSim (which counts meta-paths) ; notice the normalization step ### 3. The SemRec solution ##### 3.1 Basic idea - principle: evaluate similarity between users based on weighted and unweighted MP then infer scores from similar users - preference weights are given to different MP - difficulties: combine recommendations generated by different MP - pb1: important bias due to the fact that some types of paths are more numerous than others => similarity based on paths does not necessarily reflect similarity between objects => some kind of normalization to avoid that - pb2: we should personalize recommendations for better efficiency, but sparser data => recommendation by user groups with same preferences ##### 3.2 Recommendation with a single path - presentation of the method for one path (before generalizing) - supposing ratings from 1 to N - we have user similarity matrix for this type of specific path - compute Q_u,i,r: intensity of user u evaluating r item i from the similarity sum between users according to meta-path P_l - score predicted is the weighted average of ratings over Q_u,i,r ##### 3.3 Recommendation with multiple paths - now if we use several MP... - 3.3.1: compute weights to ratings corresponding to each type of path by minimizing mean squared error between actual and predicted scores - 3.3.2: personalized learning: each user as a weight vector, then same principle: minimizing MSE but with different weights with different users - 3.3.3: add a regularization process: learning difficult when we have few data => we use similar users ; regularization term to have the weight of a user similar to its neighbors average weight => eq 9 general form of the optimization goal ; optimization by projected gradient until convergence ##### 3.4 Discussion - general form of the objective function is L_3 in eq9 - if parameter lambda_1=0 => L_2 (equation 6) - if parameter lambda_1=infinity => L_1 (equation 4) - lambda_1 controls the level of personalization (the lower, the more personalized) - complexity analysis ### 4. Experiments ##### 4.1 Datasets - Douban (13400 u, 12700 i (= movies), 1M ratings de 1 à 5) - Yelp (16200 u, 14300 i (= buisnesses), 200K ratings de 1 à 5) - Douban clearly denser than Yelp - cf Tab.2 ##### 4.2 Metrics - accuracy evaluated with RMSE and MAE ##### 4.3 Comparison methods - 4 variants of their own model: SemRecReg (comprehensive), SemRecSgl (one type of MP), SemRecAll (same weight for everyone), SemRecInd (personalized weights, but no regularization (?)) - and methods from the literature: PMF (classic MF), SMF (PMF + reg), CMF (MF with HIN structure), HeteMF (MF + reg based on entity similarity) - no MP longer than 4 ##### 4.4 Effectiveness experiments - different settings training/test: 20/80, 40/60, 60/40, 80/20 for Douban ; 60/40, 70/30, 80/20, 90/10 for Yelp (because sparser) - Tab4: SemRecReg always have better performances in all conditions - other trends: SemRec with multiple paths in general better than SemRec with simple paths ; sparsity implies that SemRecInd is worse than SemRecAll in most circumstances (maybe I misunderstood something before) ; regularization has beneficial effects ##### 4.5 Study on cold start problem - cold start translated here by smaller training and larger test - Fig4: SemRecReg is clearly more performing in this context ##### 4.6 Study of weight preferences - explore the importance of weighted meta-paths (versus unweighted) - we can give some "slack" by imposing less constraints on scores (for example rating +/- 1) - Fig6: very demonstrative, perfs are clearly better when constraints are harder ### 5. Related work - ref 11 : HIN for data mining (Sun et Han, 2012) - ref 12 : similarity measures in HIN with PathSim (Sun et al. 2012) - ref 10 : similarity measures in HIN with HeteSim (Shi et al. 2014) - ref 2 : HIN for RS (Jamali and Lakshmanan, 2013) - the cold start question, difference depending on the technique used (MF, CF...) - ref 15 : closest to this paper, HeteRec (Yu et al., 2014) but do not use weighted paths

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