Extracting the Information Backbone in Online System
- Zhang, Qian-Ming Institute of Information Economy, Alibaba Business College, Hangzhou Normal University, Hangzhou, China
- Zeng, An Department of Physics, University of Fribourg, Fribourg, Switzerland - Institute of Information Economy, Alibaba Business College, Hangzhou Normal University, Hangzhou, China
- Shang, Ming-Sheng Web Sciences Center, School of Computer Science and Engineering, Chengdu, China - Institute of Information Economy, Alibaba Business College, Hangzhou Normal University, Hangzhou, China
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14.05.2013
Published in:
- PLoS ONE. - 2013, vol. 8, no. 5, p. e62624
English
Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature, researchers have been mainly dedicated to improving the recommendation performance (accuracy and diversity) of the algorithms while they have overlooked the influence of topology of the online user-object bipartite networks. In this paper, we find that some information provided by the bipartite networks is not only redundant but also misleading. With such “less can be more” feature, we design some algorithms to improve the recommendation performance by eliminating some links from the original networks. Moreover, we propose a hybrid method combining the time-aware and topology-aware link removal algorithms to extract the backbone which contains the essential information for the recommender systems. From the practical point of view, our method can improve the performance and reduce the computational time of the recommendation system, thus improving both of their effectiveness and efficiency.
- Faculty
- Faculté des sciences et de médecine
- Department
- Département de Physique
- Language
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- English
- Classification
- Physics
- License
- License undefined
- Identifiers
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- RERO DOC 136838
- DOI 10.1371/journal.pone.0062624
- Persistent URL
- https://folia.unifr.ch/unifr/documents/303215
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