IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v508y2018icp1-9.html
   My bibliography  Save this article

Symmetrical information filtering via punishing superfluous diffusion

Author

Listed:
  • Zhu, Xuzhen
  • Tian, Hui
  • Zhang, Tianqiao

Abstract

For niche recommendation, information filtering has attracted much attention from various fileds. Especially, mass diffusion based models behave prominently. Nevertheless, these models intrinsically suffer superfluous diffusion, transferring redundant preferences to the object and damaging the accuracy, diversity, and personalization of recommendation. Besides, we discover that the symmetrical diffusion can effectively improve recommendation performances. Thus, we assume that the superfluous diffusion should be symmetrically punished. Hence, we propose a symmetrical punishment model on superfluous diffusion for accurate information recommendation. Extensive experiments on two data sets Netflix and Movielens show that our proposed model outperforms mainstream indices remarkably in accuracy, diversity and personalization.

Suggested Citation

  • Zhu, Xuzhen & Tian, Hui & Zhang, Tianqiao, 2018. "Symmetrical information filtering via punishing superfluous diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 1-9.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:1-9
    DOI: 10.1016/j.physa.2018.05.065
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118306113
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.05.065?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Liu, Jian-Guo & Zhou, Tao & Che, Hong-An & Wang, Bing-Hong & Zhang, Yi-Cheng, 2010. "Effects of high-order correlations on personalized recommendations for bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 881-886.
    2. Jin-Hu Liu & Tao Zhou & Zi-Ke Zhang & Zimo Yang & Chuang Liu & Wei-Min Li, 2014. "Promoting Cold-Start Items in Recommender Systems," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    3. Jian-Guo Liu & Tao Zhou & Bing-Hong Wang & Yi-Cheng Zhang & Qiang Guo, 2009. "Effects Of User'S Tastes On Personalized Recommendation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 20(12), pages 1925-1932.
    4. Nie, Da-Cheng & An, Ya-Hui & Dong, Qiang & Fu, Yan & Zhou, Tao, 2015. "Information filtering via balanced diffusion on bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 44-53.
    5. Chen, Guilin & Gao, Tianrun & Zhu, Xuzhen & Tian, Hui & Yang, Zhao, 2017. "Personalized recommendation based on preferential bidirectional mass diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 397-404.
    6. Liu, Jian-Guo & Guo, Qiang & Zhang, Yi-Cheng, 2011. "Information filtering via weighted heat conduction algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2414-2420.
    7. Yanbo Zhou & Linyuan Lü & Weiping Liu & Jianlin Zhang, 2013. "The Power of Ground User in Recommender Systems," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.
    8. 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.
    9. Shang, Ming-Sheng & Jin, Ci-Hang & Zhou, Tao & Zhang, Yi-Cheng, 2009. "Collaborative filtering based on multi-channel diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4867-4871.
    10. Qiang Guo & Rui Leng & Kerui Shi & Jian-Guo Liu, 2012. "Information Filtering Via Clustering Coefficients Of User–Object Bipartite Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 23(02), pages 1-14.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miaomiao Tao & Pierre Failler & Lim Thye Goh & Wee Yeap Lau & Hanghang Dong & Liang Xie, 2022. "Quantify the Effect of China’s Emission Trading Scheme on Low-carbon Eco-efficiency: Evidence from China’s 283 Cities," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(6), pages 1-33, August.
    2. Dong, Qiang & Yuan, Quan & Shi, Yang-Bo, 2019. "Alleviating the recommendation bias via rank aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    2. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    3. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    4. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.
    5. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    6. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.
    7. Geng, Bingrui & Li, Lingling & Jiao, Licheng & Gong, Maoguo & Cai, Qing & Wu, Yue, 2015. "NNIA-RS: A multi-objective optimization based recommender system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 424(C), pages 383-397.
    8. An, Ya-Hui & Dong, Qiang & Sun, Chong-Jing & Nie, Da-Cheng & Fu, Yan, 2016. "Diffusion-like recommendation with enhanced similarity of objects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 708-715.
    9. 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.
    10. Liu, Jian-Guo & Li, Ren-De & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Collective iteration behavior for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 490-497.
    11. Liu, Jin-Hu & Zhu, Yu-Xiao & Zhou, Tao, 2016. "Improving personalized link prediction by hybrid diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 199-207.
    12. Zhang, Peng & Song, Xiaoyu & Xue, Leyang & Gu, Ke, 2019. "A new recommender algorithm on signed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 317-321.
    13. Zhang, Shujuan & Jin, Zhen & Zhang, Juan, 2016. "The dynamical modeling and simulation analysis of the recommendation on the user–movie network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 310-319.
    14. 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.
    15. Jiang, Liang-Chao & Liu, Run-Ran & Jia, Chun-Xiao, 2022. "User-location distribution serves as a useful feature in item-based collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    16. Li, Ren-De & Liu, Jian-Guo & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Social signature identification of dynamical social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 213-222.
    17. Zhang, Chu-Xu & Zhang, Zi-Ke & Yu, Lu & Liu, Chuang & Liu, Hao & Yan, Xiao-Yong, 2014. "Information filtering via collaborative user clustering modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 195-203.
    18. Grzegorz Chodak, 2020. "The problem of shelf-warmers in electronic commerce: a proposed solution," Information Systems and e-Business Management, Springer, vol. 18(2), pages 259-280, June.
    19. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Xie, Zheng & Zhang, Shengjun & Yi, Dongyun, 2015. "Predicting link directions using local directed path," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 260-267.
    20. Cui, Yaozu & Wang, Xingyuan, 2016. "Detecting one-mode communities in bipartite networks by bipartite clustering triangular," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 307-315.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:1-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.