Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm |
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Authors: | Chuang Liu Wei-Xing Zhou |
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Affiliation: | 1. Institute for the Information Economy, Hangzhou Normal University, Hangzhou, Zhejiang 310036, China;2. School of Business, East China University of Science and Technology, Shanghai 200237, China;3. Research Center for Econophysics, East China University of Science and Technology, Shanghai 200237, China;4. School of Science, East China University of Science and Technology, Shanghai 200237, China |
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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. |
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Keywords: | Recommender system Bipartite graph Network-based recommendation Recommendation accuracy Recommendation diversity |
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