SemRec: A Semantic Enhancement Framework for Tag Based Recommendation

By Guandong Xu, Yanhui Gu, Peter Dolog, Yanchun Zhang, Masaru Kitsuregawa. 2011

19 citations recorded by the source corpus.

Collaborative tagging services provided by various social web sites become popular means to mark web resources for different purposes such as categorization, expression of a preference and so on. However, the tags are of syntactic nature, in a free style and do not reflect semantics, resulting in the problems of redundancy, ambiguity and less semantics. Current tag-based recommender systems mainly take the explicit structural information among users, resources and tags into consideration, while neglecting the important implicit semantic relationships hidden in tagging data. In this study, we propose a Semantic Enhancement Recommendation strategy (SemRec), based on both structural information and semantic information through a unified fusion model. Extensive experiments conducted on two real datasets demonstarte the effectiveness of our approaches.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Julian McAuley
2. Ruining He
3. Yongfeng Zhang
4. Jure Leskovec
5. Xiangnan He
6. Xu Chen
7. Jiacheng Li
8. Jiaxi Tang

In Information Systems

1. Julian McAuley
2. Ruining He
3. Yongfeng Zhang
4. Xiangnan He
5. Xu Chen
6. Philip S. Yu
7. Jiacheng Li
8. Xue Liu

Citations in the corpus

• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “…items of personal interest from huge corpora, ranging from movies and music (Bennett and Lanning, 2007; Koenigstein, Dror, and Koren, 2011), to research articles, news and books (Das et al., 2007; Lu et al., 2015), to tags and even other users (Xu et al., 2011; Zhou et al., 2010; Zhu et al., 2011).”