Content-Based Collaborative Filtering for News Topic Recommendation

News recommendation has become a big attraction with which major Web search portals retain their users. Contentbased Filtering and Collaborative Filtering are two effective methods, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaborative...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Zhongqi Lu, Zhicheng Dou, Jianxun Lian, Xing Xie, Qiang Yang 0001
• Keywords: AAAI 2015, Intelligent Agents