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Pooja Rani, Pratima Solanki, Vedashree Puntambekar, Vivek Borse, Mrs A.S.Vaidya

Recommender systems give suggestion according to the user preferences. The number of contents and books in a university size library is enormous and a better than ever. Readers find it extremely difficult to locate their favorite books. Even though they could possibly find best preferred book by the user, finding another similar book to the first preferred book seems as if finding an in nail the ocean. That is because the second preferred book might be at very last edge of long tail. So recommender system is often a requirement in library that should be considers and need it to come into make the above finding similar. They have become fundamental applications in electronic commerce and information retrieval, providing suggestions that effectively crop large information spaces so that users are directed toward those items that best meet their needs and preferences. A variety of techniques have been suggested for performing recommendation, including collaborative technique and its three methods which are Slope One used for rating prediction, Pearson’s correlation used for finding the similarity between users and last but not the least item to item similarity. To upgrade the performance, these methods have sometimes been combined in hybrid recommendation technique.