文章基本信息

标题：Providing an Effective Collaborative Filtering Algorithm Based on Distance Measures and Neighbors' Voting
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作者：Gilda Moradi Dakhel ; Mehregan Mahdavi
期刊名称：International Journal of Computer Information Systems and Industrial Management Applications
印刷版ISSN：2150-7988
电子版ISSN：2150-7988
出版年度：2013
卷号：5
页码：524-531
出版社：Machine Intelligence Research Labs (MIR Labs)
摘要：A recommender system produces a list of suggestions for users based on their preferences. It is an intelligent system that can help users to come across their interesting items. It is widely used in personalized web systems, social networks, e-commerce and etc. It uses data mining and information filtering techniques. Mostly, recommender systems employ the collaborative filtering algorithm that is one of the most successful techniques. The collaborative filtering creates suggestions for users based on their neighbors' preferences. This algorithm suffers from its poor accuracy and scalability. This paper represents a new approach to produce a useful recommendation for an active user. It assumes that the users are m (m is the number of users) points in n dimensional space (n is the number of items) and represents a method based on users' distance. We introduce different distance measures instead of traditional similarity measures. In addition to this, we employ clustering algorithms to improve our recommendations. We use k-means clustering algorithm to categorize users based on their interests. Then our proposed algorithm introduces a new method called voting algorithm to develop a recommendation. It is based on neighbors' opinion about unknown products of the active user. This idea is similar to what happens in real life. In the real world, if there are a lot of options for us to choose from, we use other's help and make our choices based on the suggestions of our family and friends who have got the same preferences as us. We evaluate this new idea and the result of our experiments shows that the proposed algorithm is more accurate than the traditional ones; besides it is less time consuming than previous algorithms.
关键词：Recommender system; Collaborative filtering; Data ; normalization; K-means clustering; Neighbors' votes