RECOMMENDER SYSTEMS WITH SOCIAL REGULARIZATION HAO MA, DENGYONG ZHOU, CHAO LIU, MICHAEL R. LYU, IRWIN KING MENGMENG YE XIAO ZHANG LU TIAN TEXT MINING PAPER PRESENTATION

INTRODUCTION If you want to find some good movies, music, or products. What will you do?

RECOMMENDER SYSTEM

TRADITIONAL RECOMMENDER SYSTEM Assumption: Users are independent and have no relationship with each other User-Item Rating Matrix

Ask Friends SOCIAL FRIENDSHIP If you want to find some good movies, music, or products. What else will you do? Ask Friends

TRUST-AWARE RECOMMENDER SYSTEM Trust-aware Network Unilateral relationship Utilize trust information to enhance recommendation

SOCIAL RECOMMENDER SYSTEM Social Network Mutual relationship Social relationship is more informative

LOW-RANK MATRIX FACTORIZATION Goal: recover the missing ratings based on the observed ones Next, I’m going to talk about the proposed method for social recommender system, which is the main focus of this paper. To begin with, let me briefly introduce how to deal with the traditional recommender system. So one of the most widely-used approach for recommendation is via low-rank matrix factorization. The idea is straightforward. It assumes the user-item matrix R is of low rank, which can be decomposed as product of a user feature matrix and a movie feature matrix. By doing so, if we can estimate the low-dimensional feature matrices U and V correctly, then we can predict any missing entries by the inner product of the corresponding user and movie features. Accordingly, it can be formulated as the following optimization problem, where the first component measures the goodness of fit, that is how accurate the feature matrices can estimate the observed ratings, and the second component is a regularization term, similar to the regularization term for ridge regression, which can avoid overfitting. This optimization problem can be solved efficiently by gradient-based algorithm. goodness of fit avoid overfitting

SOCIAL REGULARIZATION 1 Average-based Regularization Assume each user’s taste is close to the average taste of his/her friends To incorporate the social friendship information, the authors proposed two social regularization methods. Social Network Sim(i,f) allows the user’s friends to have different tastes It is insensitive to users whose friends have diverse tastes

SOCIAL REGULARIZATION 2 Individual-based Regularization Treat each user’s friend differently based on the corresponding similarity Social Network A Large value of Sim(i,f) indicates a small distance between features Both methods can be solved efficiently via gradient-based algorithm

SIMILARITY FUNCTION Basic idea: make use of the observed ratings to compute the similarity between any two users Pearson Correlation Coefficient (PCC) centered version of cosine similarity

DATA AND STATISTICS Datasets Douban (Book/Movie/Music Review, Rating, Social Network) 129 940 users, 58 541 movies 16 830 839 ratings, 1 692 952 friend links Epinions (Consumer Review/Rating) 51 670 users, 83 509 items 631 064 ratings, 511 799 friend links

BASELINE METHODS UserMean ItemMean RSTE: trust-aware recommendation algorithm.

EXPERIMENTAL RESULTS Hide part of the observed ratings and estimate the hidden ones using different methods Evaluate based on mean absolute error (MAE) Method UserMean ItemMean RSTE SR1 SR2 Douban (80%) 0.6809 0.6288 0.5643 0.5576 0.5543 Epinion (90%) 0.9134 0.9768 0.8367 0.8287 0.8256

IMPACT OF SIMILARITY FUNCTION Sim = Ran Sim = PCC Douban 0.5579 0.5592 0.5543 Epinion 0.8324 0.8345 0.8256

MAIN TAKEAWAY Conclusion: Users' social friend information can improve recommendation accuracy Future work: Identify the most suitable group of friends to enhance recommendation accuracy Weaknesses: Social network in Douban may not be bilateral Not using k-fold cross validation

LU TIAN, XIAO ZHANG, MENGMENG YE Thank You! RECOMMENDER SYSTEMS WITH SOCIAL REGULARIZATION HAO MA, DENGYONG ZHOU, CHAO LIU, MICHAEL R. LYU, IRWIN KING LU TIAN, XIAO ZHANG, MENGMENG YE MARCH 2018