Spying Out Accurate User Preferences for Search Engine Adaptation

Abstract. Most existing search engines employ static ranking algorithms that do not adapt to the specific needs of users. Recently, some researchers have studied the use of clickthrough data to adapt a search engine's ranking function. Clickthrough data indicate for each query the results that are clicked by users. As a kind of implicit relevance feedback information, clickthrough data can easily be collected by a search engine. However, clickthrough data is sparse and incomplete, thus, it is a challenge to discover accurate user preferences from it. In this paper, we propose a novel algorithm called "Spy Na?ive Bayes" (SpyNB) to identify user preferences generated from clickthrough data. First, we treat the result items clicked by the users as sure positive examples and those not clicked by the users as unlabelled data. Then, we plant the sure positive examples (the spies) into the unlabelled set of result items and apply a na?ive Bayes classification to generate the re...

• Type: Conference
• Year: 2004
• Where: KDD
• Authors: Lin Deng, Wilfred Ng, Xiaoyong Chai, Dik Lun Lee
• Keywords: Accurate User Preferences, Clickthrough Data, Data Mining, KDD 2004, Sure Positive Examples