A Collaborative Recommender System Based on Asymmetric User Similarity
Lecture notes in computer sciencePublished 6 December 2007
Marta Millán, María Trujillo, Edward Ortiz
Citations17
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
It is found that the k-nearest neighbour algorithm may generate groups which are not necessarily homogenous, and the similarity measure used is a powerful tool for differentiating users' patterns.
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-to-user 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.
Keywords
Computer ScienceBusiness, Management and Accounting
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