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CIKM
2009
Springer

Web search result summarization: title selection algorithms and user satisfaction

10 years 9 months ago
Web search result summarization: title selection algorithms and user satisfaction
Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user’s search process. We present a machine-learned algorithm that trains a boosted tree to pick the most relevant title for a Web search result. We compare two modeling approaches: i) using absolute editorial judgments and ii) using pairwise preference judgments. We find that the pairwise modeling approach gives better results in terms of three offline metrics. We present results of our models in four regions. We also describe a hybrid user satisfaction evaluation process — search success — that combines page relevance and user click behavior, and show that our machine-learned algorithm improves in search success. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval General Terms Algorithms, Experimentation, Theory Keywords Web summarization, machine learning, user satisfaction
Tapas Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawren
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CIKM
Authors Tapas Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawrence Wai
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