IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v471y2017icp147-153.html
   My bibliography  Save this article

Improving the recommender algorithms with the detected communities in bipartite networks

Author

Listed:
  • Zhang, Peng
  • Wang, Duo
  • Xiao, Jinghua

Abstract

Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

Suggested Citation

  • Zhang, Peng & Wang, Duo & Xiao, Jinghua, 2017. "Improving the recommender algorithms with the detected communities in bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 147-153.
    • Handle: RePEc:eee:phsmap:v:471:y:2017:i:c:p:147-153
    DOI: 10.1016/j.physa.2016.11.076
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116308822
    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.2016.11.076?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. Paul Resnick & Neophytos Iacovou & Mitesh Suchak & Peter Bergstrom & John Riedl, 1994. "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Working Paper Series 165, MIT Center for Coordination Science.
    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. Zare, Hadi & Nikooie Pour, Mina Abd & Moradi, Parham, 2019. "Enhanced recommender system using predictive network approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 322-337.

    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. Lee, Charles M.C. & Ma, Paul & Wang, Charles C.Y., 2015. "Search-based peer firms: Aggregating investor perceptions through internet co-searches," Journal of Financial Economics, Elsevier, vol. 116(2), pages 410-431.
    2. Shuang-Bo Sun & Zhi-Heng Zhang & Xin-Ling Dong & Heng-Ru Zhang & Tong-Jun Li & Lin Zhang & Fan Min, 2017. "Integrating Triangle and Jaccard similarities for recommendation," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-16, August.
    3. 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.
    4. 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.
    5. Sohn, Jeong Woong & Kim, Jin Ki, 2020. "Factors that influence purchase intentions in social commerce," Technology in Society, Elsevier, vol. 63(C).
    6. Zhang, Yi & Robinson, Douglas K.R. & Porter, Alan L. & Zhu, Donghua & Zhang, Guangquan & Lu, Jie, 2016. "Technology roadmapping for competitive technical intelligence," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 175-186.
    7. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    8. Zhang, Jing & Peng, Qinke & Sun, Shiquan & Liu, Che, 2014. "Collaborative filtering recommendation algorithm based on user preference derived from item domain features," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 66-76.
    9. Chen, Jianrui & Wei, Lidan & Uliji, & Zhang, Li, 2018. "Dynamic evolutionary clustering approach based on time weight and latent attributes for collaborative filtering recommendation," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 8-18.
    10. Hausmann, Ricardo & Stock, Daniel P. & Yıldırım, Muhammed A., 2022. "Implied comparative advantage," Research Policy, Elsevier, vol. 51(8).
    11. Hael Al-bashiri & Mansoor Abdullateef Abdulgabber & Awanis Romli & Hasan Kahtan, 2018. "An improved memory-based collaborative filtering method based on the TOPSIS technique," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-26, October.
    12. Su, Zhan & Zheng, Xiliang & Ai, Jun & Shen, Yuming & Zhang, Xuanxiong, 2020. "Link prediction in recommender systems based on vector similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 560(C).
    13. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    14. Park, Youngjin & Yoon, Janghyeok, 2017. "Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 170-183.
    15. Muhammed A. Yildirim, 2014. "Implied Comparative Advantage," CID Working Papers 276, Center for International Development at Harvard University.
    16. 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.
    17. Liebig, Jessica & Rao, Asha, 2016. "Predicting item popularity: Analysing local clustering behaviour of users," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 523-531.
    18. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    19. Ai, Jun & Cai, Yifang & Su, Zhan & Zhang, Kuan & Peng, Dunlu & Chen, Qingkui, 2022. "Predicting user-item links in recommender systems based on similarity-network resource allocation," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    20. Moradi, Parham & Ahmadian, Sajad & Akhlaghian, Fardin, 2015. "An effective trust-based recommendation method using a novel graph clustering algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 462-481.

    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:471:y:2017:i:c:p:147-153. 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.