期刊
EPL
卷 111, 期 4, 页码 -出版社
EPL ASSOCIATION, EUROPEAN PHYSICAL SOCIETY
DOI: 10.1209/0295-5075/111/48007
关键词
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资金
- National Natural Science Foundation of China [61471060, 61433014, 61302077, 11222543]
- Funds for Creative Research Groups of China [61421061]
- BUPT Excellent PhD Students Foundation [CX201433]
- Program for New Century Excellent Talents in University [NCET-11-0070]
- Special Project of Sichuan Youth Science and Technology Innovation Research Team [2013TD0006]
Recently, in physical dynamics, mass-diffusion-based recommendation algorithms on bipartite network provide an efficient solution by automatically pushing possible relevant items to users according to their past preferences. However, traditional mass-diffusion-based algorithms just focus on unidirectional mass diffusion from objects having been collected to those which should be recommended, resulting in a biased causal similarity estimation and not-so-good performance. In this letter, we argue that in many cases, a user's interests are stable, and thus bidirectional mass diffusion abilities, no matter originated from objects having been collected or from those which should be recommended, should be consistently powerful, showing unbiased consistence. We further propose a consistence-based mass diffusion algorithm via bidirectional diffusion against biased causality, outperforming the state-of-the-art recommendation algorithms in disparate real data sets, including Netflix, MovieLens, Amazon and Rate Your Music. Copyright (C) EPLA, 2015
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