›› 2011, Vol. 26 ›› Issue (5): 754-766.doi: 10.1007/s11390-011-0175-2

Special Issue: Surveys

• Special Section on Community Analysis and Information Recommendation • Previous Articles     Next Articles

Personalized News Recommendation: A Review and an Experimental Investigation

Lei Li1 (李磊), Ding-Ding Wang1 (王丁丁), Shun-Zhi Zhu2 (朱顺痣), and Tao Li1 (李涛)   

  1. 1. School of Computing and Information Sciences, Florida International University, Miami, Florida 33199, U.S.A.
    2. Department of Computer Science and Technology, Xiamen University of Technology, Xiamen 361024, China
  • Received:2011-02-21 Revised:2011-06-14 Online:2011-09-05 Published:2011-09-05
  • Contact: Tao Li E-mail:lli003@cs.fiu.edu, dwang003@cs.fiu.edu; szzhu@xmut.edu.cn; taoli@cs.fiu.edu
  • About author:Lei Li received his M.S. degree in software engineering from Beihang University in 2008. He is currently a Ph.D. candidate in School of Computing and Information Sciences at Florida International University. His research interests include data mining and machine learning.
    Ding-Ding Wang received her Bachelor's degree from the Department of Computer Science, University of Science and Technology of China in 2003, and her Ph.D. degree in computer science in 2009 from Florida International University. She is currently a postdoctoral researcher in the Center for Computational Science at University of Miami. Her research interests are data mining and information retrieval.
    Shun-Zhi Zhu received his Ph.D. degree in control theory and engineering in 2007 from Xiamen University. He is currently an associate professor and vice chair of the Department of Computer Science & Technology at Xiamen University of Technology. His research interests are information systems, GIS, and data mining.
    Tao Li received his Ph.D. degree in computer science in 2004 from the University of Rochester. He is currently an associate professor in the School of Computer Science at Florida International University. His research interests are in data mining, machine learning and information retrieval. He is a recipient of USA NSF CAREER Award and multiple IBM Faculty Research Awards.
  • Supported by:

    This work is partially supported by the National Science Foundation of US under Grant Nos. IIS-0546280 and CCF-0830659, and the National Natural Science Foundation of China under Grant No. 61070151.

Online news articles, as a new format of press releases, have sprung up on the Internet. With its convenience and recency, more and more people prefer to read news online instead of reading the paper-format press releases. However, a gigantic amount of news events might be released at a rate of hundreds, even thousands per hour. A challenging problem is how to efficiently select specific news articles from a large corpus of newly-published press releases to recommend to individual readers, where the selected news items should match the reader's reading preference as much as possible. This issue refers to personalized news recommendation. Recently, personalized news recommendation has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Existing personalized news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. A variety of techniques have been proposed to tackle personalized news recommendation, including content-based, collaborative filtering systems and hybrid versions of these two. In this paper, we provide a comprehensive investigation of existing personalized news recommenders. We discuss several essential issues underlying the problem of personalized news recommendation, and explore possible solutions for performance improvement. Further, we provide an empirical study on a collection of news articles obtained from various news websites, and evaluate the effect of different factors for personalized news recommendation. We hope our discussion and exploration would provide insights for researchers who are interested in personalized news recommendation.

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