A social recommendation framework based on multi-scale continuous conditional random fields

Abstract

This paper addresses the issue of social recommendation based on collaborative filtering (CF) algorithms. Social recommendation emphasizes utilizing various attributes information and relations in social networks to assist recommender systems. Although recommendation techniques have obtained distinct developments over the decades, traditional CF algorithms still have these following two limitations: (1) relational dependency within predictions, an important factor especially when the data is sparse, is not being utilized effectively; and (2) straightforward methods for combining features like linear integration suffer from high computing complexity in learning the weights by enumerating the whole value space, making it difficult to combine various information into an unified approach. In this paper, we propose a novel model, Multi-scale Continuous Conditional Random Fields (MCCRF), as a framework to solve above problems for social recommendations. In MCCRF, relational dependency within predictions is modeled by the Markov property, thus predictions are generated simultaneously and can help each other. This strategy has never been employed previously. Besides, diverse information and relations in social network can be modeled by state and edge feature functions in MCCRF, whose weights can be optimized globally. Thus both problems can be solved under this framework. In addition, We propose to utilize Markov chain Monte Carlo (MCMC) estimation methods to solve the difficulties in training and inference processes of MCCRF. Experimental results conducted on two real world data have demonstrated that our approach outperforms traditional CF algorithms. Additional experiments also show the improvements from the two factors of relational dependency and feature combination, respectively.

A social analytics platform for smarter commerce solutions

“…There are a few related works in the literature [30][31][32][33][34][35]. Although Xin et al [33] refers to social recommendation, the authors do not really use social network information. They are computing recommendation based on similar users.…”

Boosting social network connectivity with link revival

“…Recently many work used the social connections among people and their social activities to improve the item recommendation results [8,14,29,18]. These work all focused on improving the item recommendation quality by considering the social relationships between the users in the system.…”

Improving Matching Process in Social Network

“…The rating information is used in collaborative approach. However, other information such as user demographic information, user relations in social networks is not integrated into the existing systems [19].…”