Rare query expansion via Wikipedia for sponsored search
|Rare query expansion via Wikipedia for sponsored search|
|Author(s)||Xu Z., Wang X., Yu Y.|
|Editor(s)||Wang Y.Li T.|
|Published in||Advances in Intelligent and Soft Computing|
|Keyword(s)||Pseudo-relevance Feedback, Query Expansion, Sponsored Search, Wikipedia (Extra: External resources, Pseudo relevance feedback, Query augmentations, Query expansion, Sponsored Search, Sponsored searches, State-of-the-art algorithms, Web searches, Wikipedia, Algorithms, Expansion, Feedback, Intelligent systems, Knowledge engineering, Web services, Websites)|
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Sponsored Search has evolved as the delivery of relevant, targeted text advertisements for Web queries. To match the most relevant advertisements for queries, query expansion algorithms were deeply researched during previous works. While most of current state-of-the-art algorithms appeal to Web search results as external resources to expand queries, we propose a novel approach based on Wikipedia for query augmentation against rare queries in sponsored search. By retrieving the top-k relevant articles in Wikipedia with Web query, we can extract more representative information and form a new ad query for the web query. With the new ad query, more relevant advertisements can be identified. To verify the effectiveness of our wiki-based query expansion methodology, we design a set of experiments and the results turn out that our approach is very effective for rare queries in sponsored search.
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