A reconsideration of negative ratings for network-based recommendation

Liang Hu, Liang Ren and Wenbin Lin

Physica A: Statistical Mechanics and its Applications, 2018, vol. 490, issue C, 690-701

Abstract: Recommendation algorithms based on bipartite networks have become increasingly popular, thanks to their accuracy and flexibility. Currently, many of these methods ignore users’ negative ratings. In this work, we propose a method to exploit negative ratings for the network-based inference algorithm. We find that negative ratings play a positive role regardless of sparsity of data sets. Furthermore, we improve the efficiency of our method and compare it with the state-of-the-art algorithms. Experimental results show that the present method outperforms the existing algorithms.

Keywords: Recommender systems; Bipartite network; Negative ratings (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:490:y:2018:i:c:p:690-701

DOI: 10.1016/j.physa.2017.08.119

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