Proposing a New Metric for Collaborative Filtering

Arash Bahrehmand, Reza Rafeh

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DOI: 10.4236/jsea.2011.47047                

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.

Keywords

Recommendation Systems, Collaborative filtering, Similarity Metric

Conflicts of Interest

The authors declare no conflicts of interest.

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