Weighted difference entropy based similarity measure at two levels in a recommendation framework

Presenting small chunks of interesting information to a target user from the large pool of information available on World Wide Web are the primary task of a recommender system. Memory based Collaborative Filtering generates recommendations based on preferences of those users whose past preferences are similar to the current preferences of the target user. These users collectively form the Nearest Neighbor Set (NN Set) of the target user. The better the selection of NN Set, the better is the generation of recommendations. In the proposed scheme, the user ratings (preferences) on the available items are divided into two levels. Level I consist of ratings on popular items and Level II consist of ratings on unpopular items. The proposed similarity measure, “TSimD(UX1,UX2)”, between two users is based on weighted difference entropy. Modified memory based Collaborative Filtering calculates the proposed similarity measure at both the levels to improve the selection of users in the NN Set of the target user. It selects those users in the NN Set who are more similar to the target user with respect to items at Level II as compared to similarity between them with respect to items at Level I. The results on Movie Lens dataset depicted that the proposed similarity measure had higher accuracy than the Weighted Difference Entropy based similarity measure which worked on the entire preferences of the users. The proposed similarity measure was also compared with other similarity measures (like Cosine, Pearson Correlation, Spearman and Rating Frequency based Similarity Measure) which were also obtained at two levels.

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