Product recommendation and rating prediction based on multi-modal social networks

Abstract

Online Social Rating Networks (SRNs) such as Epinions and Flixter, allow users to form several implicit social networks, through their daily interactions like co-commenting on the same products, or similarly co-rating products. The majority of earlier work in Rating Prediction and Recommendation of products (e.g. Collaborative Filtering) mainly takes into account ratings of users on products. However, in SRNs users can also built their explicit social network by adding each other as friends. In this paper, we propose Social-Union, a method which combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit SRNs. Moreover, we propose an effective weighting strategy of SRNs influence based on their structured density. We also generalize our model for combining multiple social networks. We perform an extensive experimental comparison of the proposed method against existing rating prediction and product recommendation algorithms, using synthetic and two real data sets (Epinions and Flixter). Our experimental results show that our Social-Union algorithm is more effective in predicting rating and recommending products in SRNs.

Connecting Researchers with Companies for University-Industry Collaboration

“…Both academia and industry have put in much effort to design efficient algorithms and improve the performance of recommendation systems. There are three main streams of recommendation systems: content-based recommendation systems, collaborative filtering recommendation systems and hybrid recommendation systems [13,17,28,33]. Content-based recommendation systems recommend items that are similar to user's previous preference.…”

Scalable similarity-based neighborhood methods with MapReduce

“…In future work we intend to explore how our method could be used to scale out recommendation approaches that incorporate similarity computations on large networks [16,25].…”

Social Network and Tag Sources Based Augmenting Collaborative Recommender System

“…He and Chu [4] developed a Bayesian network-based recommender system (SNRS-BN) and study the performance of SNRS-BN under different types of social relationships. Panagiotis Symeonidis et al [5] proposed the Social-Union which combines similarity matrices derived from heterogeneous explicit or implicit SRNs. Apart from friendships, membership is another kind of social relations.…”

“…That means only few small values of λ can be chosen for equivalence. Therefore, in this case, some transformations are needed for the two matrixes [5]. We first make transformations to all similarity values of the two matrixes by the following formula:…”