Personalized News Recommendation: A Review and an Experimental Investigation |
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Authors: | Lei Li Ding-Ding Wang Shun-Zhi Zhu Tao Li |
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Affiliation: | (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 |
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Abstract: | 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 effciently
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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