Personalized Search Based on User Search Histories
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
The Journal of Machine Learning Research
Prioritized aggregation of multiple context dimensions in mobile IR
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Most present day search engines have a deterministic behavior in the sense that they return the same search results for all users who submit the same query at a certain time. They do not take the user!s interests and preferences into account in the retrieval process. Integrating user context in the retrieval process can help deliver more targeted search results, thereby providing a personalized search experience to the user. Personalizing web search involves the process of identifying user interests during interaction with the user, and then using that information to deliver results that are more relevant to the user. In this paper, we present our approach to personalizing web search on a mobile device (iPhone). Our approach involves building an ontological model of user interests on the user!s mobile device based on his interaction with web search results. Personalization of search results is achieved by re-ranking search results returned by a standard search engine (Yahoo) based on proximity to the user!s interest model. The ability to recognize user interests in a completely non-invasive way and the accuracy of personalized results are some of the major advantages of our approach.