Comments:

# Summary ### Introduction ##### about hybrid reco : - many RS have an underlying HIN structure and are achieving hybrid reco (in the sense using both user feedbacks and content-based info) ##### difference with existing methods : - use different types of relations, benefit : use the fact that users consume an item for different reasons (e.g. : movie for genre, for director, etc) ##### Recommender System : - combine users feedback and various types of info in a collaborative filtering style - use metapath in HIN to generate reco - technical implementation uses Matrix Factorization ##### Datasets : - MovieLens 100K combined with IMDB and Yelp, implicit feedback only ##### Contributions: - study reco with implicit feedback in HIN - use network heterogeneity to spread preferences on the metapaths - generate personalized reco - specific case study : ML100K and Yelp ### Background and preliminaries ##### binary user feedback - explain how to generate the bipartite adjacency matrix ##### Heterogeneous Information Network - definition (using entity mapping function and link entity mapping function) - vocabulary to describe HIN ##### Matrix Factorization for implicit feedback - describe principle of MF (decomposing the feedback matrix) - resolution using NMF ##### Problem definition - how to make personalized recommendation based on implicit feedback in the form of a list of recommendations ### Meta-path based latent features ##### meta-path - definition and interest (types of paths in a HIN) - can be used to measure similarity and proximity between entities - ex: user [watches] movie [watched by] user [following] actor [starring] movie ##### user preference diffusion - type of meta-paths considered in the paper : user -> item -> * -> item (* may be tag, genre, director, plot for ML100K ; * may be category, customer, location for Yelp) - define user preference score : normalized weighted sum of the number of paths to a given item (eq 2) - if L types of metapaths, then L matrices R (user preference matrices) - use these scores to build the recommendation model ##### global recommendation model - define the recommendation mechanic which is inspired by MF - (?) MF may be achieved on each user preference matrix taken separately : find a couple of reduced matrices with NMF, then prediction model is given by equation 4 - RK: not personalized as coefficients are the same for every user ### Personalized recommendation model - same principle as global recommendation method, except that there is first a clustering, and the learning is achieved cluster per cluster - the number of clusters is a parameter of the method ### Model learning with implicit feedback - learning the model parameters (thetas in equation 4) - use implicit feedback to do so (1 = user browses item / 0 = user does not) - usually prediction done with either classification or learning-to-rank but their approach: rank 1s above 0s (in the spirit of ref 21) ##### Bayesian ranking-based optimization - assumption: a user ranking is independent from the others (allow to get eq.7) - assumption on the probability expressed in equation 8 - allows to derive the expression of objective function O ##### optimization algorithm - optimization: finds thetas such that dO/dTheta = 0 - method: Stochastic Gradient Descent ##### learning personalized recommendation models - this technique is not personalized - to personalize the reco: clusters with a k-means method ### Empirical study ##### Data - dataset 1 : IMDB + ML100K (IM100K); if user has seen movie 1 else 0 - dataset 2 : Yelp; if user has reviewed buisness 1 else 0 - d2 much sparser than d1 (see feedback distribs on figure 5) - temporal split 80% / 20% between training and test ##### Competitors and evaluation metrics - RS benchmarks: popularity-based, co-click, NMF (baseline of collaborative filtering), hybrid SVM - for their method: 10 different metapaths différentes (see Table 6.2) - evaluation: as based on implicit feedback, precision at position and top-10 mean reciprocal rank (MRR_k) ##### Performance comparison - Table 3 for a summary - very few items interact with a lot of users - parameters for NMF: dimension of the reduced matrix: 20 (IM100K), 60 (Yelp) - Hybrid-SVM uses the same info as their method (HeteRec) and uses PathSim - in general HeteRec better than all benchmark methods - in particular HeteRec > Hybrid-SVM (while similar information) - improvement higher for Yelp than for IM100K, possibly a consequence of Yelp sparsity - HeteRec-p (personalized version) : even better than HeteRec-g ##### Performance analysis - more precise analysis of the performances on IM100K only for HeteRec-g , HeteRec-p , NMF , Co-Click - divide in 6 different training sets, depending on various parameters - performances increase with the number of movies watched for all methods except co-click - performances decrease with movies popularity for all methods except co-click ##### Parameter tuning - HeteRec have more parameters - regularization parameter lambda (eq 9) computed with cross-validation - sampling necessary for Yelp (as 10^12 elements), performance variations with sampling represented on Fig7 (relatively stable) - for HeteRec-p: number of clusters, see fgure 6c ### Related work ##### CF based hybrid RS ##### Information network analysis
Alt-Tab at 2019-07-01 09:24:19
Edited by Alt-Tab at 2019-07-01 10:14:46

You comment anonymously! You will not be able to edit/delete the comment.

Please consider to register or login.

Use $\LaTeX$ to type formulæ and markdown to format text.
When you post something to which you hold the copyright you authorise us to do distribute this data across the scientific community. You can post public domain content. All user-generated content will be freely available online. Please see this page to learn more about Papersγ's terms of use and privacy policy.