标题:A search index-enhanced feature model for news recommendation
作者:Chen, Kun; Ji, Xiaowen; Wang, Huaiqing
作者机构:[Chen, Kun; Wang, Huaiqing] South Univ Sci & Technol, Shenzhen, Peoples R China.; [Ji, Xiaowen] Shandong Univ Sci & Technol, Qingdao, Peoples R Chin 更多
通讯作者:Chen, Kun
通讯作者地址:[Chen, K]South Univ Sci & Technol, Shenzhen, Peoples R China;[Chen, K]South Univ Sci & Technol, Financial Math & Financial Engn, Shenzhen 518055, Peop 更多
来源:JOURNAL OF INFORMATION SCIENCE
出版年:2017
卷:43
期:3
页码:328-341
DOI:10.1177/0165551516639801
关键词:Classification; feature generation; news recommendation; topic model
摘要:General news recommendations are important but have received limited attention because of the difficulties of measuring public interest. In public search engines, the objects of search terms reflect the issues that interest or concern search engine users. Because of the popularity of search engines, search indexes have become a new measure for describing public interest trends. With the help of a public search index provided by search engines, we construct a news topic search feature and a news object search feature. These features measure the public attention on key elements of the news. In the experiment, we compare various feature models with machine learning algorithms with respect to financial news recommendations. The results demonstrate that the topic search features perform best compared with other feature models. This research contributes to both the feature generation and news recommendation domains.
收录类别:EI;SCIE;SSCI
资源类型:期刊论文
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