Volume 111, Number 4, August 2015
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||11 September 2015|
Personalized recommendation based on unbiased consistence
1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications Beijing, 100876, China
2 CompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China Chengdu, 610054, China
3 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu, 610054, China
(a) email@example.com (corresponding author)
(b) firstname.lastname@example.org (corresponding author)
Received: 19 March 2015
Accepted: 10 August 2015
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.
PACS: 89.65.-s – Social and economic systems / 89.75.Hc – Networks and genealogical trees / 89.75.-k – Complex systems
© EPLA, 2015
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