Tag Recommendation Based on Continuous Conditional Random Fields
Xin Liu, Yang Wang, Ziang Liu, Zhen Yuan, Maoqiang Xie, Yalou Huang · 2009
This paper focuses on tag recommendation. In many tagging systems, tags are highly interdependent. Conventional methods do not exploit dependencies between tags when computing the relevance score of a candidate tag for a new resource. In this paper, we take into consideration the relationship between candidate tags and propose a Continuous Conditional Random Fields (CRF) model for tag recommendation, referred to as TR-CRF. Firstly, a feature vector is set up for each candidate tag, which contains tag co-occurrence information. Then a ranking model is trained with TR-CRF, and the ranking score represents the relevance between a candidate tag and a resource. With the use of this model, tags of a new resource are generated automatically according to their ranking scores. Experimental results on two real world tag recommendation tasks validate the effectiveness of our method.