A vertex similarity index for better personalized recommendation
This work addresses the need for better recommendation algorithms to handle information overload, but it is incremental as it builds on existing similarity-based methods.
The authors tackled the problem of improving personalized recommendation by proposing a new vertex similarity index called CosRA, which combines the cosine and resource-allocation indices, and demonstrated that it outperforms benchmark methods in accuracy, diversity, and novelty on datasets like MovieLens, Netflix, and RYM.
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.