A nonnegative Matrix Tri-Factorization Technique for Recommendation in Microblog

作者：Zhang Guoying^*; Cai Guanghui; Wu Hao; Zheng Shuwen

来源：6th International Conference on Machinery, Materials, Environment, Biotechnology and Computer (MMEBC), 2016-06-11 to 2016-06-12.

• Micro-blogging communities
• Recommendation system
• Demographic information
• Genre information

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.

• 出版日期2016
• 单位云南大学