Exploring relevance for clicks

Mining feedback information from user click-through data is an important issue for modern Web retrieval systems in terms of architecture analysis, performance evaluation and algorithm optimization. For commercial search engines, user click-through data contains useful information as well as large amount of inevitable noises. This paper proposes an approach to recognize reliable and meaningful user clicks (referred to as Relevant Clicks, RCs) in click-through data. By modeling user clickthrough behavior on search result lists, we propose several features to separate RCs from click noises. A learning algorithm is presented to estimate the quality of user clicks. Experimental results on large scale dataset show that: 1) our model effectively identifies RCs in noisy click-through data; 2) Different from previous click-through analysis efforts, our approach works well for both hot queries and long-tail queries. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: I...

• Type: Conference
• Year: 2009
• Where: CIKM
• Authors: Rongwei Cen, Yiqun Liu, Min Zhang, Bo Zhou, Liyun Ru, Shaoping Ma
• Keywords: CIKM 2009, Click-through Data, Database, User Click-through Data, User Clicks