Behavior patterns of online users and the effect on information filtering
- Zhang, Cheng-Jun Department of Physics, University of Fribourg, Switzerland
- Zeng, An Department of Physics, University of Fribourg, Switzerland
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13.10.2011
Published in:
- Physica A: Statistical Mechanics and its Applications. - 2012, vol. 391, no. 4, p. 1822–1830
English
Understanding the structure and evolution of web-based user-item bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus on investigating the patterns of online users’ behavior and the effect on recommendation process. Empirical analysis on the e-commercial systems show that users’ taste preferences are heterogeneous in general but their interests for niche items are highly clustered. Additionally, recommendation processes are investigated on both the real networks and the reshuffled networks in which real users’ behavior patterns can be gradually destroyed. We find that the performance of personalized recommendation methods is strongly related to the real network structure. Detailed study on each item shows that most hot items are accurately recommended and their recommendation accuracy is robust to the reshuffling process. However, the accuracy for niche items is relatively low and drops significantly after removing users’ behavior patterns. Our work is also meaningful in practical sense since it reveals an effective direction to improve the accuracy and the robustness of the existing recommender systems.
- 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 29340
- DOI 10.1016/j.physa.2011.09.038
- Persistent URL
- https://folia.unifr.ch/unifr/documents/302638
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