Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm

Chuang Liu and Wei-Xing Zhou

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 22, 5704-5711

Abstract: Network-based recommendation algorithms for user–object link predictions have achieved significant developments in recent years. For bipartite graphs, the resource reallocation in such algorithms is analogous to heat spreading (HeatS) or probability spreading (ProbS) processes. The best algorithm to date is a hybrid of the HeatS and ProbS techniques with homogeneous initial resource configurations, which fulfills simultaneously high accuracy and large diversity requirements. We investigate the effect of heterogeneity in initial configurations on the HeatS + ProbS hybrid algorithm and find that both recommendation accuracy and diversity can be further improved in this new setting. Numerical experiments show that the improvement is robust.

Keywords: Recommender system; Bipartite graph; Network-based recommendation; Recommendation accuracy; Recommendation diversity (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:22:p:5704-5711

DOI: 10.1016/j.physa.2012.06.034

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