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B. M. Sarwar, G. Karypis, J. A. Konstan and J. Riedl, “Analysis of Recommendation Algorithms for E-Commerce,” ACM Conference on Electronic Commerce, New York, 17-20 October 2000, pp. 158-167.

has been cited by the following article:

• TITLE: Proposing a New Metric for Collaborative Filtering

  AUTHORS: Arash Bahrehmand, Reza Rafeh

  KEYWORDS: Recommendation Systems, Collaborative filtering, Similarity Metric

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.4 No.7, July 12, 2011

  ABSTRACT: The aim of a recommender system is filtering the enormous quantity of information to obtain useful information based on the user’s interest. Collaborative filtering is a technique which improves the efficiency of recommendation systems by considering the similarity between users. The similarity is based on the given rating to data by similar users. However, user’s interest may change over time. In this paper we propose an adaptive metric which considers the time in measuring the similarity of users. The experimental results show that our approach is more accurate than the traditional collaborative filtering algorithm.