Advertising Keywords Recommendation for Short-Text Web Pages Using Wikipedia

被引:27
作者
Zhang, Weinan [1 ]
Wang, Dingquan [1 ]
Xue, Gui-Rong [2 ]
Zha, Hongyuan [3 ]
机构
[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200240, Peoples R China
[2] Aliyun Com, Hangzhou, Zhejiang, Peoples R China
[3] Georgia Inst Technol, Coll Comp, Atlanta, GA 30032 USA
关键词
Contextual advertising; advertising keywords recommendation; topic-sensitive PageRank; Wikipedia; Algorithms; Experimentation;
D O I
10.1145/2089094.2089112
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Advertising keywords recommendation is an indispensable component for online advertising with the keywords selected from the target Web pages used for contextual advertising or sponsored search. Several ranking-based algorithms have been proposed for recommending advertising keywords. However, for most of them performance is still lacking, especially when dealing with short-text target Web pages, that is, those containing insufficient textual information for ranking. In some cases, short-text Web pages may not even contain enough keywords for selection. A natural alternative is then to recommend relevant keywords not present in the target Web pages. In this article, we propose a novel algorithm for advertising keywords recommendation for short-text Web pages by leveraging the contents of Wikipedia, a user-contributed online encyclopedia. Wikipedia contains numerous entities with related entities on a topic linked to each other. Given a target Web page, we propose to use a content-biased PageRank on the Wikipedia graph to rank the related entities. Furthermore, in order to recommend high-quality advertising keywords, we also add an advertisement-biased factor into our model. With these two biases, advertising keywords that are both relevant to a target Web page and valuable for advertising are recommended. In our experiments, several state-of-the-art approaches for keyword recommendation are compared. The experimental results demonstrate that our proposed approach produces substantial improvement in the precision of the top 20 recommended keywords on short-text Web pages over existing approaches.
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页数:25
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