Identifying "best bet" web search results by mining past user behavior

@inproceedings{citeulike:1542261, abstract = {The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-of-the-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques.}, address = {New York, NY, USA}, author = {Agichtein, Eugene and Zheng, Zijian }, booktitle = {KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {1542261}, doi = {10.1145/1150402.1150526}, isbn = {1595933395}, keywords = {en, log-mining, social-search, www-search}, pages = {902--908}, posted-at = {2008-01-16 02:15:14}, priority = {2}, publisher = {ACM Press}, title = {Identifying "best bet" web search results by mining past user behavior}, url = {http://portal.acm.org/citation.cfm?id=1150526}, year = {2006} }

TY - CONF ID - citeulike:1542261 L3 - citeulike-article-id:1542261 TI - Identifying "best bet" web search results by mining past user behavior T2 - KDD '06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 902 EP - 908 AD - New York, NY, USA PB - ACM Press SN - 1595933395 N2 - The top web search result is crucial for user satisfaction with the web search experience. We argue that the importance of the relevance at the top position necessitates special handling of the top web search result for some queries. We propose an effective approach of leveraging millions of past user interactions with a web search engine to automatically detect "best bet" top results preferred by majority of users. Interestingly, this problem can be more effectively addressed with classification than using state-of-the-art general ranking methods. Furthermore, we show that our general machine learning approach achieves precision comparable to a heavily tuned, domain-specific algorithm, with significantly higher coverage. Our experiments over millions of user interactions for thousands of queries demonstrate the effectiveness and robustness of our techniques. KW - en KW - log-mining KW - social-search KW - www-search AU - Agichtein, Eugene AU - Zheng, Zijian PY - 2006/// UR - http://portal.acm.org/citation.cfm?id=1150526 ER -