IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v586y2022ics0378437121007640.html
   My bibliography  Save this article

User-location distribution serves as a useful feature in item-based collaborative filtering

Author

Listed:
  • Jiang, Liang-Chao
  • Liu, Run-Ran
  • Jia, Chun-Xiao

Abstract

Personalized recommender system is a powerful method to solve the problem of information overload, which has been widely applied in a variety of scenarios, such as e-commerce, video platforms and social networks, to help users find relevant items or friends of interest. Collaborative filtering is the most successful and widely used algorithm in the recommender systems as its powerful capability of generating recommendations by sharing collective experiences of users. In recent years, the use of mobile devices and the rapid development of internet infrastructures provide the possibility to analyze regional features of items based on user locations. Here we improve the performance of collaborative filtering by using user-location distribution to uncover the potential similarities between items. We find that the similarity of user-location distribution is one efficient measure for the item–item similarities in the framework of collaborative filtering to generate personalized recommendation for users. Furthermore, we have also mixed similarity measures of user-location distribution and the traditional method based on the number of common users linearly to optimize the performance of collaborative filtering. Based on the Movielens data set, we show that the performance of our methods could be improved in terms of the metrics of accuracy and diversity simultaneously.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:phsmap:v:586:y:2022:i:c:s0378437121007640
    DOI: 10.1016/j.physa.2021.126491
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121007640
    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.2021.126491?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. Jian-Guo Liu & Tao Zhou & Bing-Hong Wang & Yi-Cheng Zhang & Qiang Guo, 2010. "Degree Correlation Of Bipartite Network On Personalized Recommendation," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(01), pages 137-147.
    2. Li, Man & Wen, Luosheng & Chen, Feiyu, 2021. "A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    3. Hou, Lei & Liu, Kecheng & Liu, Jianguo & Zhang, Runtong, 2017. "Solving the stability–accuracy–diversity dilemma of recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 415-424.
    4. 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.
    5. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    6. Liu, Run-Ran & Liu, Jian-Guo & Jia, Chun-Xiao & Wang, Bing-Hong, 2010. "Personal recommendation via unequal resource allocation on bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3282-3289.
    7. Liu, Run-Ran & Jia, Chun-Xiao & Zhou, Tao & Sun, Duo & Wang, Bing-Hong, 2009. "Personal recommendation via modified collaborative filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(4), pages 462-468.
    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. Hou, Lei & Huang, Yichen, 2024. "Optimizing the connectedness of recommendation networks for retrieval accuracy and visiting diversity of random walks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(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. 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.
    3. 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.
    4. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    5. Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Xiaoji Wan & Fen Chen & Hailin Li & Weibin Lin, 2022. "Potentially Related Commodity Discovery Based on Link Prediction," Mathematics, MDPI, vol. 10(19), pages 1-27, October.
    11. 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.
    12. 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.
    13. Sundaresan Bhaskaran & Raja Marappan & Balachandran Santhi, 2021. "Design and Analysis of a Cluster-Based Intelligent Hybrid Recommendation System for E-Learning Applications," Mathematics, MDPI, vol. 9(2), pages 1-21, January.
    14. 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.
    15. Lee, Yan-Li & Dong, Qiang & Zhou, Tao, 2021. "Link prediction via controlling the leading eigenvector," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    16. Zhang, Sheng-Tai & Yuan, Hao-Yu & Duan, Ling-Li, 2020. "Analysis of human behavior statistics law based on WeChat Moment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

    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:586:y:2022:i:c:s0378437121007640. 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.