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

  • Authors:

  • Xin Xin;Irwin King;Hongbo Deng;Michael R. Lyu

  • Affiliations:

  • The Chinese University of Hong Kong, Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong, Hong Kong;The Chinese University of Hong Kong, Hong Kong, Hong Kong

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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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.