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

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 w...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Xin Xin, Irwin King, Hongbo Deng, Michael R. Lyu
• Keywords: CIKM 2009, Database, Relational Dependency, Social Recommendations, Traditional Cf Algorithms