文章基本信息

标题：A Web Recommendation System based on Individual Preference Estimated from Twitter
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作者：Takeshi Toda ; Mizuho Sawada
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2014
卷号：11
期号：5
出版社：IJCSI Press
摘要：This paper proposes a web recommendation system that estimates and dynamically updates individual preference with twitter, in order to reduce web search effort. The proposed system gathers personal comments on twitter, extracts object-predicate pairs by text analysis, and ranks the objects with weighting of the paired predicates in accordance with a prepared predicate-point dictionary such as glike so much (+5 points)h and ghate (-5 points).h We implemented the proposed system on a server in our laboratory using Twitter API for getting comments on Twitter, Yahoo API! for the text analysis and Bing API for the web search. In an experiment, we evaluated recall and precision of the objects ranking obtained by the proposed system. We also evaluated a precision of web page recommendation searched by top-ranked object. From the experimental result, the proposed web recommendation system provided higher relevance ratio compared to that of conventional system.
关键词：Individual Preference; Micromedia; Recommendation; Text Analysis; Twitter; Web Search