ClickRank: Learning Session-Context Models to Enrich Web Search Ranking

摘要

User browsing information, particularly non-search-related activity, reveals important contextual information on the preferences and intents of Web users. In this article, we demonstrate the importance of mining general Web user behavior data to improve ranking and other Web-search experience, with an emphasis on analyzing individual user sessions for creating aggregate models. In this context, we introduce Click-Rank, an efficient, scalable algorithm for estimating Webpage and Website importance from general Web user-behavior data. We lay out the theoretical foundation of ClickRank based on an intentional surfer model and discuss its properties. We quantitatively evaluate its effectiveness regarding the problem of Web-search ranking, showing that it contributes significantly to retrieval performance as a novel Web-search feature. We demonstrate that the results produced by ClickRank for Web-search ranking are highly competitive with those produced by other approaches, yet achieved at better scalability and substantially lower computational costs. Finally, we discuss novel applications of ClickRank in providing enriched user Web-search experience, highlighting the usefulness of our approach for nonranking tasks.