Smoothing Clickthrough Data for Web Search Ranking

Smoothing Clickthrough Data for Web Search Ranking

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by Jianfeng Gao , Wei Yuan , Xiao Li , Kefeng Deng , Jian-yun Nie

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Datum	Value	Source
TITLE	Smoothing Clickthrough Data for Web Search Ranking	SVM HeaderParse 0.2
AUTHOR NAME	Jianfeng Gao	SVM HeaderParse 0.2
AUTHOR AFFIL	*Microsoft Research, Redmond, USA,	SVM HeaderParse 0.2
AUTHOR NAME	Wei Yuan	SVM HeaderParse 0.2
AUTHOR AFFIL	*Microsoft Research, Redmond, USA,	SVM HeaderParse 0.2
AUTHOR NAME	Xiao Li	SVM HeaderParse 0.2
AUTHOR AFFIL	*Microsoft Research, Redmond, USA,	SVM HeaderParse 0.2
AUTHOR NAME	Kefeng Deng	SVM HeaderParse 0.2
AUTHOR AFFIL	*Microsoft Research, Redmond, USA,	SVM HeaderParse 0.2
AUTHOR NAME	Jian-yun Nie	SVM HeaderParse 0.2
AUTHOR AFFIL	*Microsoft Research, Redmond, USA,	SVM HeaderParse 0.2
ABSTRACT	Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web search applications. Such benefits, however, are severely limited by the data sparseness problem, i.e., many queries and documents have no or very few clicks. The ranker thus cannot rely strongly on clickthrough features for document ranking. This paper presents two smoothing methods to expand clickthrough data: query clustering via Random Walk on click graphs and a discounting method inspired by the Good-Turing estimator. Both methods are evaluated on real-world data in three Web search domains. Experimental results show that the ranking models trained on smoothed clickthrough features consistently outperform those trained on unsmoothed features. This study demonstrates both the importance and the benefits of dealing with the sparseness problem in clickthrough data.	SVM HeaderParse 0.2
CITATIONS	28 found	ParsCit 1.0