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A framework for expressing and combining preferences
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Proceedings of the 13th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
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ACM Transactions on Internet Technology (TOIT)
Dynamic agglomerative-divisive clustering of clickthrough data for collaborative web search
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
A retrieval method adaptively reducing user's subjective impression gap
Multimedia Tools and Applications
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This article addresses search engine personalization. We present a new approach to mining a user's preferences on the search results from clickthrough data and using the discovered preferences to adapt the search engine's ranking function for improving search quality. We develop a new preference mining technique called SpyNB, which is based on the practical assumption that the search results clicked on by the user reflect the user's preferences but does not draw any conclusions about the results that the user did not click on. As such, SpyNB is still valid even if the user does not follow any order in reading the search results or does not click on all relevant results. Our extensive offline experiments demonstrate that SpyNB discovers many more accurate preferences than existing algorithms do. The interactive online experiments further confirm that SpyNB and our personalization approach are effective in practice. We also show that the efficiency of SpyNB is comparable to existing simple preference mining algorithms.