A probabilistic model based approach for blended search

In this paper, we propose to model the blended search problem by assuming conditional dependencies among queries, VSEs and search results. The probability distributions of this model are learned from search engine query log through unigram language model. Our experimental exploration shows that, (1) a large number of queries in generic Web search have vertical search intentions; and (2) our proposed algorithm can effectively blend vertical search results into generic Web search, which can improve the Mean Average Precision (MAP) by as much as 16% compared to traditional Web search without blending. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: Information Search and Retrieval – Retrieval models General Terms Algorithms, Verification. Keywords Vertical search, blended search, language model, query log.

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Ning Liu, Jun Yan, Zheng Chen
• Keywords: Blended Search, Generic Web Search, Internet Technology, Vertical Search, WWW 2009