Content-Based Collaborative Filtering for News Topic Recommendation

By Zhongqi Lu, Zhicheng Dou, Jianxun Lian, Xing Xie, Qiang Yang. 2015

156 citations recorded by the source corpus.

News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, 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 collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

How researchers see this paper

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

In Computer Networks and Communications

1. Xing Xie
2. Ahmad Abdollahzadeh Barforoush
3. Yongfeng Huang
4. Hossein Shirazi
5. Fangzhao Wu
6. Ruining He
7. Julian McAuley
8. Sina Dami

In Information Systems

1. Fangzhao Wu
2. Chuhan Wu
3. Xing Xie
4. Yongfeng Huang
5. Julian McAuley
6. Ruining He
7. Mingxiao An
8. Jianqiang Huang

In Management Science and Operations Research

1. Julian McAuley
2. Ruining He
3. Xing Xie
4. Chuhan Wu
5. Yongfeng Huang
6. Fangzhao Wu
7. Jianqiang Huang
8. Mingxiao An

Citations in the corpus

• VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “Others have developed content-based and hybrid models that make use of a variety of information sources, including text (and context), taxonomies, and user demographics (Bao, Fang, and Zhang, 2014; Kanagal et al., 2012; Lu et al., 2015; Qiao et al., 2014).”
• Ieee Transactions on Knowledge and Data Engineering 1 towards Bayesian Deep Learning: a Framework and Some Existing Methods (unknown): “As another example, to achieve high accuracy in recommender systems (RS) [1], [39], [40], [50], [67], we need to fully understand the content of the items (e.g., documents and movies) [46], analyze the profile and preferences of users [70], [73], and evaluate the similarity among the users [3],…”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “Existing news recommendation methods are usually based on collaborative filtering (CF) techniques and news content[20–22, 24].”
• News events prediction using Markov logic networks (2018, background): “While some related works focused on predicting news importance (news impact/volume) [6–8] and news interests (news filtering/recommendation) [9–11], other works dealt with predicting dynamic content on the web, often using time-series [12] and state-space models [13].”
• A Survey on Bayesian Deep Learning (2016, unknown): “Take recommender systems [1, 70, 71, 92, 121] as another example.”