Personal recommendation via unequal resource allocation on bipartite networks

Run-Ran Liu, Jian-Guo Liu, Chun-Xiao Jia and Bing-Hong Wang

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 16, 3282-3289

Abstract: In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent β is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations.

Keywords: Personal recommendation; Unequal resource allocation; Bipartite network (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:16:p:3282-3289

DOI: 10.1016/j.physa.2010.04.004

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