IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v416y2014icp192-197.html
   My bibliography  Save this article

Improved hybrid information filtering based on limited time window

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114006864
    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.2014.08.008?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, 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    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.
    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. 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.
    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. 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.

    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. Wen, Yuan & Liu, Yun & Zhang, Zhen-Jiang & Xiong, Fei & Cao, Wei, 2014. "Compare two community-based personalized information recommendation algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 199-209.
    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. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    4. 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.
    5. Zhang, Yi-Lu & Guo, Qiang & Ni, Jing & Liu, Jian-Guo, 2015. "Memory effect of the online rating for movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 261-266.
    6. 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.
    7. 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.
    8. Kart, Ozge & Ulucay, Oguzhan & Bingol, Berkay & Isik, Zerrin, 2020. "A machine learning-based recommendation model for bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Liu, Xiao-Lu & Guo, Qiang & Hou, Lei & Cheng, Can & Liu, Jian-Guo, 2015. "Ranking online quality and reputation via the user activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 629-636.
    15. Zhou, Bin & He, Zhe & Wang, Nianxin & Xi, Zhendong & Li, Yujian & Wang, Bing-Hong, 2015. "On the optimization of multitasking process with multiplayer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 41-45.
    16. Wei, Yun & Tian, Qing & Guo, Jianhua & Huang, Wei & Cao, Jinde, 2019. "Multi-vehicle detection algorithm through combining Harr and HOG features," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 130-145.
    17. Gao, Jian & Zhou, Tao, 2017. "Evaluating user reputation in online rating systems via an iterative group-based ranking method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 546-560.
    18. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    19. Hao Liao & An Zeng & Rui Xiao & Zhuo-Ming Ren & Duan-Bing Chen & Yi-Cheng Zhang, 2014. "Ranking Reputation and Quality in Online Rating Systems," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
    20. Zhang, Yin & Gao, Kening & Zhang, Bin, 2015. "The concept exploration model and an application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 430-442.

    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:416:y:2014:i:c:p:192-197. 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.