In a recommendation system, users provide description of their interests through initial search keywords and the system recommends items based on these keywords. A user is satisfied if it finds the item of its choice and the system benefits, otherwise the user explores an item from the recommended list. Usually when the user explores an item, it picks an item that is nearest to its interest from the list. While the user explores an item, the system recommends new set of items. This continues till either the user finds the item of its interest or quits. In all, the user provides ample chances and feedback for the system to learn its interest. The aim of this paper is to efficiently utilize user responses to recommend items and find the item of user's interest quickly. We first derive optimal policies in the continuous Euclidean space and adapt the same to the space of discrete items. We propose the notion of local angle in the space of discrete items and develop user response-local angle (UR-LA) based recommendation policies. We compare the performance of UR-LA with widely used collaborative filtering (CF) based policies on two real datasets and show that UR-LA performs better in majority of the test cases. We propose a hybrid scheme that combines the best features of both UR-LA and CF (and history) based policies, which outperforms them in most of the cases. Towards the end, we propose alternate recommendation policies again utilizing the user responses, based on clustering techniques. These policies outperform the previous ones, and are computationally less intensive. Further, the clustering based policies perform close to theoretical limits.
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