IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v444y2016icp713-721.html
   My bibliography  Save this article

Personalized recommendation based on heat bidirectional transfer

Author

Listed:
  • Ma, Wenping
  • Feng, Xiang
  • Wang, Shanfeng
  • Gong, Maoguo

Abstract

Personalized recommendation has become an increasing popular research topic, which aims to find future likes and interests based on users’ past preferences. Traditional recommendation algorithms pay more attention to forecast accuracy by calculating first-order relevance, while ignore the importance of diversity and novelty that provide comfortable experiences for customers. There are some levels of contradictions between these three metrics, so an algorithm based on bidirectional transfer is proposed in this paper to solve this dilemma. In this paper, we agree that an object that is associated with history records or has been purchased by similar users should be introduced to the specified user and recommendation approach based on heat bidirectional transfer is proposed. Compared with the state-of-the-art approaches based on bipartite network, experiments on two benchmark data sets, Movielens and Netflix, demonstrate that our algorithm has better performance on accuracy, diversity and novelty. Moreover, this method does better in exploiting long-tail commodities and cold-start problem.

Suggested Citation

  • Ma, Wenping & Feng, Xiang & Wang, Shanfeng & Gong, Maoguo, 2016. "Personalized recommendation based on heat bidirectional transfer," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 713-721.
    • Handle: RePEc:eee:phsmap:v:444:y:2016:i:c:p:713-721
    DOI: 10.1016/j.physa.2015.10.068
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437115009279
    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.2015.10.068?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. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    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.
    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. 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.
    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).

    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. Christian Peukert, 2019. "The next wave of digital technological change and the cultural industries," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 43(2), pages 189-210, June.
    2. Dongjing Chen & Xiaotong Guo, 2023. "Impact of the Digital Economy and Financial Development on Residents’ Consumption Upgrading: Evidence from Mainland China," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    3. Miguel Godinho de Matos & Pedro Ferreira, 2020. "The Effect of Binge-Watching on the Subscription of Video on Demand: Results from Randomized Experiments," Information Systems Research, INFORMS, vol. 31(4), pages 1337-1360, December.
    4. Kai Huschelrath & Kathrin Muller, 2014. "The Value Of Bluer Skies. – How Much Do Consumers Gain From Entry By Jetblue Airways In Long-Haul U.S. Airline Markets?," Articles, International Journal of Transport Economics, vol. 41(1).
    5. Fenner, Trevor & Levene, Mark & Loizou, George, 2010. "Predicting the long tail of book sales: Unearthing the power-law exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2416-2421.
    6. Néstor Duch-Brown & Lukasz Grzybowski & André Romahn & Frank Verboven, 2023. "Evaluating the Impact of Online Market Integration—Evidence from the EU Portable PC Market," American Economic Journal: Microeconomics, American Economic Association, vol. 15(4), pages 268-305, November.
    7. Ting Li & Robert J. Kauffman & Eric van Heck & Peter Vervest & Benedict G. C. Dellaert, 2014. "Consumer Informedness and Firm Information Strategy," Information Systems Research, INFORMS, vol. 25(2), pages 345-363, June.
    8. Timothy J. Richards & Elliot Rabinovich, 2018. "The long‐tail of online grocery shopping," Agribusiness, John Wiley & Sons, Ltd., vol. 34(3), pages 509-523, June.
    9. 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.
    10. David M. Byrne & John G. Fernald & Marshall B. Reinsdorf, 2016. "Does the United States Have a Productivity Slowdown or a Measurement Problem?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 109-182.
    11. Anindya Ghose & Arun Sundararajan, 2005. "Versioning and Quality Distortion in Software? Evidence from E-Commerce Panel Data," Working Papers 05-14, NET Institute, revised Oct 2005.
    12. He, Wentao & Hao, Xiaoli, 2023. "Competition and welfare effects of introducing new products into the new energy vehicle market: Empirical evidence from Tesla’s entry into the Chinese market," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    13. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    14. Schneider, Matthew J. & Gupta, Sachin, 2016. "Forecasting sales of new and existing products using consumer reviews: A random projections approach," International Journal of Forecasting, Elsevier, vol. 32(2), pages 243-256.
    15. Huanxing Yang, 2013. "Targeted search and the long tail effect," RAND Journal of Economics, RAND Corporation, vol. 44(4), pages 733-756, December.
    16. Baojun Jiang & Kinshuk Jerath & Kannan Srinivasan, 2011. "Firm Strategies in the "Mid Tail" of Platform-Based Retailing," Marketing Science, INFORMS, vol. 30(5), pages 757-775, September.
    17. Eric Overby & Sandy Jap, 2009. "Electronic and Physical Market Channels: A Multiyear Investigation in a Market for Products of Uncertain Quality," Management Science, INFORMS, vol. 55(6), pages 940-957, June.
    18. Argentesi, Elena & Buccirossi, Paolo & Cervone, Roberto & Duso, Tomaso & Marrazzo, Alessia, 2021. "The effect of mergers on variety in grocery retailing," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 79, pages 1-19.
    19. Gensler, Sonja & Leeflang, Peter & Skiera, Bernd, 2012. "Impact of online channel use on customer revenues and costs to serve: Considering product portfolios and self-selection," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 192-201.
    20. Karl Taeuscher, 2019. "Uncertainty kills the long tail: demand concentration in peer-to-peer marketplaces," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(4), pages 649-660, December.

    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:444:y:2016:i:c:p:713-721. 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.