Papers^γ / Alt-Tab

# In short Short article considering HIN-based recommendation. The contribution consists in using not only meta-paths in the HIN, but also "enhanced" meta-paths which are related to more elaborate motifs (typically triangles) # Summary ### 1. Introduction - usually HIN-based RS rely on the number pf meta-paths from u to i, the more there are, the higher the recommendation - Figure 1 summarizes situations that the authors want to distinguish: (u1,b4) and (u1,b3) are equivalent in terms of (P2) meta-paths, but not in terms of 3-nodes based "trust" patterns - patterns here are considered using 3 nodes patterns (à la Milo et al) as represented in Figure 2, meta-paths based on these motifs are called Motif Enhanced Meta Path (MEMP) - the concept is explored on 2 datasets: Epinions and CiaoDVD ### 2. Framework - to compute similarity using MP, we build the adjacency matrix W_{Ai,Aj} where Ai and Aj are types, then we build the commuting matrix Cp which is a product of the adjacency matrices corresponding to path P => similarities are based on counting obtained by matrix products ##### MoHINRec framework - they define adj matrix based on the same principle, but an edge is considered if it belongs to a pattern of interest (illustrated on Figure 3) - equation 2: they merge different matrices (obtained with usual MPs or MEMPs) with an alpha-weighting - then they test this with state-of-the-art HIN-based RS (actually, one they have designed in another work) ### 3. Experiments and analysis - 2 datasets, described in Table 1: - Epinions: ~22K users, 300K items, 900K ratings - CiaoDVD: ~17K users, 16K items, 72K ratings - evaluation metrics: MAE, RMSE (accuracy based) - baselines for comparison: RegSVD (MF with regularization), SoReg (MF using social links for regularization), SocialMF (MF with social trust propagation), FMG (IN-based RS, state-of-the-art) - supposedly better than the others - Settings: 8/1/1 train-validation-test, 5 experiment series with different splits ##### Performance comparison: - Summary in Table 2: MoHINRec outperforms the others - depending on the motif considered, we should vary the alpha coefficient to obtain the best possible performance (it is not clear for me how they tune alpha) - performances vary depending on the dataset and morif considered - FMG is still more efficient than othher benchmark methods ### 4. Related work ##### 4.1 HIN based recommendation ##### 4.2 motifs (very complex networks based: Milo et al., recent works by Leskovec et al.

### Short summary ##### Plan - procedure for article selection - recommender systems overview - review itself: diversity measure ; impact of diversification on recommendation ; diversification methods - conclusion and perspectives ##### Procedure for article selection - search on Google Scholar with keywords selection - doubles elimination - selection of articles without additional payments - clustering into three groups of articles (based on review plan) ##### RS overview (standard, aiming at people new to the field) - dates back to Salton and McGill, 1980 (ref 1) - usual standard techniques: word vectors, DT, Naïve Bayes, kNN, SVM - applications: digital TV, web multimedia (YouTube, Shelfari (now merged into Goodreads), Facebook, Goodreads), personalized ads, online shopping - the general process of recommendation: past users activity collection ; create user model ; present recommendation information ; feedback collection (distinguishing explicit and implicit recommendation) - important challenges: data sparsity (working with "mostly empty user-items datasets") ; cold start (new users or items in the dataset ; overfitting (actually, rather in the sense of overspecialization) ##### Diversification - Table 1 summarizes diversity measures - Bradley-Smyth 2001: average dissimilarity between all pairs of items - Fleder-Hosanagar 2007: Gini - explore with a model how diversity evolves through recommendation cycles - Clarke et al. 2008: combined measure (ambiguity, redundancy, novelty...) - Vargas et al. 2011: intralist diversity - Hu-Pu 2011: perceived diversity (questionnaire) - Vargas et al. 2012: in the line of Clarke et al. 2008 - Castagnos et al. 2013: in the line of Bradley-Smyth 2001 - develop experiments with users - L'Huillier et al. 2014: idem - Vargas et al. 2014: binomial diversity (mixing coverage and redundancy) - Table 2 summarizes how diversity affects recommendation - usually: F-measure, MAE, NMAE - some articles show that diversification by reranking is possible without affecting too much accuracy (ex: Adomavicius et Kwon, 51) - some address the question of trade-off between diversity and accuracy (52: Hurley-Zhang 2011, 55: Aytekin-Karakaya 2014, 56: Ekstrand et al, 2014, 57: Javari-Jalili, 2014) - pb seen as multi-objective, looking for Pareto efficient ranking (58: Ribeiro et al 2015) - Table 3 summarizes diversification algorithms - many methods are reranking from accuracy-based ranking (59: Ziegler et al. 2005, 51 et 61: Adomavicius-Kwon 2012, 2011, 62: Premchaiswadi et al 2013) - then various strategies, depending on the method, on the type of data, whether the authors question temporal aspects - underlying idea is that the original algorithm (typically CF) is already diverse, just needs reordering ##### Conclusions - no consensus on a diversity metric - increasing diversity does not necessarily means sacrifice accuracy - various challenges: not enough live studies ; work in psychology would be useful ; how to use systems which have a lot of different types of items ; how to diversify during the reco process (and not a posteriori)

# 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