Context-aware search personalization with concept preference

As the size of the web is growing rapidly, a well-recognized challenge for developing web search engines is to optimize the search result towards each user's preference. In this paper, we propose and develop a new personalization framework that captures the user's preference in the form of concepts obtained by mining web search contexts. The search context consists of both the user's clickthroughs and query reformulations that satisfy some specific information need, which is able to provide more information than each individual query in a search session. We also propose a method that discovers search contexts by one-pass of raw search query log. Using the information of the search context, we develop eight strategies that derive conceptual preference judgment. A learning-to-rank approach is employed to combine the derived preference judgments and then a Context-Aware User Profile (CAUP) is created. We further employ CAUP to adapt a personalized ranking function. Experimental results demonstrate that our approach captures accurate and comprehensive user's preference and, in terms of Top-N results quality, outperforms those existing concept-based personalization approaches without using search contexts.