Effects of the bipartite structure of a network on performance of recommenders

Qing-Xian Wang, Jian Li, Xin Luo, Jian-Jun Xu and Ming-Sheng Shang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 492, issue C, 1257-1266

Abstract: Recommender systems aim to predict people’s preferences for online items by analyzing their historical behaviors. A recommender can be modeled as a high-dimensional and sparse bipartite network, where the key issue is to understand the relation between the network structure and a recommender’s performance. To address this issue, we choose three network characteristics, clustering coefficient, network density and user-item ratio, as the analyzing targets. For the cluster coefficient, we adopt the Degree-preserving rewiring algorithm to obtain a series of bipartite network with varying cluster coefficient, while the degree of user and item keep unchanged. Furthermore, five state-of-the-art recommenders are applied on two real datasets. The performances of recommenders are measured by both numerical and physical metrics. These results show that a recommender’s performance is positively related to the clustering coefficient of a bipartite network. Meanwhile, higher density of a bipartite network can provide more accurate but less diverse or novel recommendations. Furthermore, the user-item ratio is positively correlated with the accuracy metrics but negatively correlated with the diverse and novel metrics.

Keywords: Bipartite network; Clustering coefficient; Network density; User-item ratio; Recommender system (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:492:y:2018:i:c:p:1257-1266

DOI: 10.1016/j.physa.2017.11.053

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