Abstract — As the amount of Web information grows rapidly, search engines must be able to retrieve information according to the user’s preference. In this paper, we propose a new web search personalization approach that captures the user’s interests and preferences in the form of concepts by mining search results and their clickthroughs. Due to the important role location information plays in mobile search, we separate concepts into content concepts and location concepts, and organize them into ontologies to create an ontology-based, multi-facet (OMF) profile to precisely capture the user’s content and location interests and hence improve the search accuracy. Moreover, recognizing the fact that different users and queries may have different emphases on content and location information, we introduce the notion of content and location entropies to measure the amount of content and location information associated with a query, and click content and location entropies to measure how much the user is interested in the content and location information in the results. Accordingly, we propose to define personalization effectiveness based on the entropies and use it to balance the weights between the content and location facets. Finally, based on the derived ontologies and personalization effectiveness, we train an SVM to adapt a personalized ranking function for re-ranking of future search. We conduct extensive experiments to compare the precision produced by our OMF profiles and that of a baseline method. Experimental results show that OMF improves the precision significantly compared to the baseline. I.

Imagine a system that can push highly selective information right to our hands when and only when we need it. This requires a mind-reading machine, but unfortunately we don’t have one — yet. User profiling attempts to estimate what is most important to a user at a particular point in time and space. In this talk, I will start with simple raw data such as the users ’ queries and clicks on the web and places they have visited to estimate what they might be interested in. We further divide user interests into content-based and location-based. We discuss issues involving the transformation of raw activities to conceptual needs, identifying user groups for collaborative filtering and the roles of locations in personalized information delivery.

Abstract-The main problem that the web search currently faces is that search queries are short and ambiguous and thus are unable to meet user needs. To avoid such problems, some search engines suggest terms that are meaningfully related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user’s conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. Experimental results show that our approach has better precision and recall than the existing query clustering methods. Keywords—Click-through, ranking, personalization, conceptbased clustering, query clustering I.