A vertex similarity index for better personalized recommendation
arXiv:1510.02348 · doi:10.1016/j.physa.2016.09.057
Abstract
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.
11 pages, 3 figures, 2 tables in Physica A, 2016
References in corpus (11)
- Predicting Missing Links via Local Information
- Heat Conduction Process on Community Networks as a Recommendation Model
- Power-law Strength-Degree Correlation From a Resource-Allocation Dynamics on Weighted Networks
- Effect of initial configuration on network-based recommendation
- Recommendation model based on opinion diffusion
- Information filtering via preferential diffusion
- Personal Recommendation via Modified Collaborative Filtering
- Evaluating user reputation in online rating systems via an iterative group-based ranking method
- Group-based ranking method for online rating systems with spamming attacks
- Promoting cold-start items in recommender systems
- Ultra accurate collaborative information filtering via directed user similarity
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