Kernel based Collaborative Topic Regression for Tag Recommendation

DOI

10.2991/icesame-16.2016.24How to use a DOI?

Keywords

Kernel; Collaborative Topic Regression; Tag Recommendation.

Abstract

Tag recommendation is very helpful for users to organize or categorize online resources like music, photos and articles. In recent years, some models, such as collaborative topic regression (CTR) and its variants, have demonstrated promising performance for tag recommendation. In this paper, we propose a novel Bayesian model, called Kernel based CTR (KCTR) to combine kernel based probabilistic matrix factorization which exploits social networks with topic modeling. In contrast to CTR and its existing variants, KCTR model is capable of keeping the real correlation among items rather than ideally assuming the relations among items are mutually independent, which is hardly satisfied in real world. Experimental results on two real datasets show that our method outperforms the state-of-the-art approaches.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yanwei Guo
AU  - Hongrong Cheng
AU  - Mingshuang Tang
AU  - Jiaqing Luo
AU  - Shijie Zhou
PY  - 2016/03
DA  - 2016/03
TI  - Kernel based Collaborative Topic Regression for Tag Recommendation
BT  - Proceedings of the 2016 International Conference on Education, Sports, Arts and Management Engineering
PB  - Atlantis Press
SP  - 113
EP  - 117
SN  - 2352-5398
UR  - https://doi.org/10.2991/icesame-16.2016.24
DO  - 10.2991/icesame-16.2016.24
ID  - Guo2016/03
ER  -