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# 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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# 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.

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