1 research outputs found
A vertex similarity index for better personalized recommendation
Recommender systems benefit us in tackling the problem of information
overload by predicting our potential choices among diverse niche objects. So
far, a variety of personalized recommendation algorithms have been proposed and
most of them are based on similarities, such as collaborative filtering and
mass diffusion. Here, we propose a novel vertex similarity index named CosRA,
which combines advantages of both the cosine index and the resource-allocation
(RA) index. By applying the CosRA index to real recommender systems including
MovieLens, Netflix and RYM, we show that the CosRA-based method has better
performance in accuracy, diversity and novelty than some benchmark methods.
Moreover, the CosRA index is free of parameters, which is a significant
advantage in real applications. Further experiments show that the introduction
of two turnable parameters cannot remarkably improve the overall performance of
the CosRA index.Comment: 11 pages, 3 figures, 2 tables in Physica A, 201