Exploiting Clicks as Implicit Judgments to Examine the Potential for Personalization
Web search engines have been helping users to locate information they need on the web. But they are far from optimal in that they only return general search results for different people who issue the same query to them, regardless of the differences in interest among individuals. In this paper we try to explore how different people perceive search results for the same query by mining clicks, which acts as a proxy for relevance, from a large body of American Online (AOL) query log data. In an attempt to investigate the variability1 in what people are searching for when they issue the same query, we only select queries from query log for which we have clicks from at least eigbt individuals. Extensive analysis of clicks on search results for the same query reveals that there are variations in judgments across individuals as shown in the potential for personalization curve. It shows that there is an observable gap between individual preferences and best group preferences for the results of the same query and this gap increases as the number of people in the group increases. This tells us how much room there is to improve the search result through personalization.
web search click search result potential for personalization implicit judgment
Fikadu Gemechu Erba Zhang Yu Liu Ting
Department of Computer Science and Technology Harbin Institute of Technology Harbin, PR, China
国际会议
海口
英文
464-468
2011-07-15（万方平台首次上网日期，不代表论文的发表时间）