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Information Retrieval

Researchers from IIT  focusing on information retrieval and recommender systems are working on several trending issues like query expansion for web search, xml database searching, collaborative filtering, and emotion based search result ranking etc. In query expansion research they aim to personalize the web search queries of a user by extracting and integrating information from his social network. For xml database searching they are working on answering forest queries and devising different methods for improving existing tree edit distance algorithms and subtree ranking algorithms for that purpose. Collaborative filtering algorithms for recommender systems are really popular and they are working to trust based and temporal collaborative filtering algorithms and looking forward to improve the mean absolute error, precision, recall and hit rate of existing solutions. They are also working on the evolution of recommender systems, on which they have a very few existing literatures. They have developed several new hypotheses on this issue and investigating their effectiveness empirically. Moreover, another important information retrieval task is being addressed by them, which is emotion based search result ranking. They are trying to re-rank the result set returned by existing popular search engines based on the emotion of a user using it and finally produce a personal experience for the user.