A nonnegative Matrix Tri-Factorization Technique for Recommendation in Microblog
- DOI
- 10.2991/mmebc-16.2016.293How to use a DOI?
- Keywords
- Micro-blogging communities, Recommendation system, Demographic information, Genre information.
- Abstract
Nowadays, more and more users share news and information in micro-blogging communities such as Twitter, Tumblr or Plurk. With the increasing number of registered users in this kind of sites, finding relevant and reliable sources of information becomes essential. For recommending sufficient information for a user, it is important to extract automatically user's interest with accuracy and to collect new information which interests and attracts the user. To address this problem, we propose a social matrix tri-factorization model with two auxiliary matrices to improve the accuracy of recommendation in microblog. We construct two matrices on the users and items respectively, to exploit the user's demographic information and item's genre information which play an important role in microblog recommendation system. Experimental results show that our method achieves a higher performance and provide more effective results than the state-of -art methods especially when recommending informations. We also show that out model captures user interests more precisely and gives better recommendation interpretability.
- 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/).
Cite this article
TY - CONF AU - Guoying Zhang AU - Guanghui Cai AU - Hao Wu AU - Shuwen Zheng PY - 2016/06 DA - 2016/06 TI - A nonnegative Matrix Tri-Factorization Technique for Recommendation in Microblog BT - Proceedings of the 2016 6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer PB - Atlantis Press SP - 1436 EP - 1439 SN - 2352-5401 UR - https://doi.org/10.2991/mmebc-16.2016.293 DO - 10.2991/mmebc-16.2016.293 ID - Zhang2016/06 ER -