A collaborative recommender system based on asymmetric user similarity

Authors:
Marta Millan;Maria Trujillo;Edward Ortiz
Affiliations:
School of Systems and Computer Engineering of Universidad del Valle, Cali, Colombia;School of Systems and Computer Engineering of Universidad del Valle, Cali, Colombia;School of Systems and Computer Engineering of Universidad del Valle, Cali, Colombia
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 26
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Abstract

Recommender systems could be seen as an application of a data mining process in which data collection, pre-processing, building user profiles and evaluation phases are performed in order to deliver personalised recommendations. Collaborative filtering systems rely on user-touser similarities using standard similarity measures. The symmetry of most standard similarity measures makes it difficult to differentiate users' patterns based on their historical behaviour. That means, they are not able to distinguish between two users when one user' behaviour is quite similar to the other but not vice versa. We have found that the k-nearest neighbour algorithm may generate groups which are not necessarily homogenous. In this paper, we use an asymmetric similarity measure in order to distinguish users' patterns. Recommendations are delivered based on the users' historical behaviour closest to a target user. Preliminary experimental results have shown that the similarity measure used is a powerful tool for differentiating users' patterns.