GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
Clustering with Lower Bound on Similarity
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
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Recommender systems employ the popular K-nearest neighbor collaborative filtering (K-CF) methodology and its variations for recommending the products. In K-CF approach, recommendation for a given user is computed based on the ratings of K-nearest neighbors. In K-CF approach, it can be noted that, the system identifies K neighbors for each user irrespective of the number of products he/she has rated. As a result, the user who have rated few products may get the less-similar neighbors and the user who have rated more products may miss the genuine neighbors. In the literature, the notion of lower-bound similarity has been proposed to improve the clustering performance in which the clusters are extracted by fixing the similarity threshold. In this paper, we have extended the notion of lower-bound similarity to recommender systems to improve the performance of K-CF approach. In the proposed approach, instead of fixing K for finding the neighborhood, the similarity threshold value is fixed to extract the neighbors for each user. As a result, each user gets appropriate number of neighbors based on the number of products rated by him/her in a dynamic manner. The experimental results, on MovieLens dataset, show that the proposed lower bound similarity CF approach improves the performance of recommender systems over K-CF approach.