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Improved hybrid information filtering based on limited time window

Author

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  • Song, Wen-Jun
  • Guo, Qiang
  • Liu, Jian-Guo

Abstract

Adopting the entire collecting information of users, the hybrid information filtering of heat conduction and mass diffusion (HHM) (Zhou et al., 2010) was successfully proposed to solve the apparent diversity–accuracy dilemma. Since the recent behaviors are more effective to capture the users’ potential interests, we present an improved hybrid information filtering of adopting the partial recent information. We expand the time window to generate a series of training sets, each of which is treated as known information to predict the future links proven by the testing set. The experimental results on one benchmark dataset Netflix indicate that by only using approximately 31% recent rating records, the accuracy could be improved by an average of 4.22% and the diversity could be improved by 13.74%. In addition, the performance on the dataset MovieLens could be preserved by considering approximately 60% recent records. Furthermore, we find that the improved algorithm is effective to solve the cold-start problem. This work could improve the information filtering performance and shorten the computational time.

Suggested Citation

  • Song, Wen-Jun & Guo, Qiang & Liu, Jian-Guo, 2014. "Improved hybrid information filtering based on limited time window," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 192-197.
    • Handle: RePEc:eee:phsmap:v:416:y:2014:i:c:p:192-197
    DOI: 10.1016/j.physa.2014.08.008
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    References listed on IDEAS

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    1. Guo, Qiang & Song, Wen-Jun & Hou, Lei & Zhang, Yi-Lu & Liu, Jian-Guo, 2014. "Effect of the time window on the heat-conduction information filtering model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 15-21.
    2. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    3. Qian-Ming Zhang & An Zeng & Ming-Sheng Shang, 2013. "Extracting the Information Backbone in Online System," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-7, May.
    4. Zhang, Cheng-Jun & Zeng, An, 2012. "Behavior patterns of online users and the effect on information filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1822-1830.
    5. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    6. Tian Qiu & Zi-Ke Zhang & Guang Chen, 2013. "Information Filtering via a Scaling-Based Function," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-10, May.
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    Cited by:

    1. Li, Wen-Jun & Dong, Qiang & Shi, Yang-Bo & Fu, Yan & He, Jia-Lin, 2017. "Effect of recent popularity on heat-conduction based recommendation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 334-343.
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    3. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.

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