IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v483y2017icp273-279.html
   My bibliography  Save this article

Personalized recommendation via unbalance full-connectivity inference

Author

Listed:
  • Ma, Wenping
  • Ren, Chen
  • Wu, Yue
  • Wang, Shanfeng
  • Feng, Xiang

Abstract

Recommender systems play an important role to help us to find useful information. They are widely used by most e-commerce web sites to push the potential items to individual user according to purchase history. Network-based recommendation algorithms are popular and effective in recommendation, which use two types of elements to represent users and items respectively. In this paper, based on consistence-based inference (CBI) algorithm, we propose a novel network-based algorithm, in which users and items are recognized with no difference. The proposed algorithm also uses information diffusion to find the relationship between users and items. Different from traditional network-based recommendation algorithms, information diffusion initializes from users and items, respectively. Experiments show that the proposed algorithm is effective compared with traditional network-based recommendation algorithms.

Suggested Citation

  • Ma, Wenping & Ren, Chen & Wu, Yue & Wang, Shanfeng & Feng, Xiang, 2017. "Personalized recommendation via unbalance full-connectivity inference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 273-279.
  • Handle: RePEc:eee:phsmap:v:483:y:2017:i:c:p:273-279
    DOI: 10.1016/j.physa.2017.04.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303588
    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.2017.04.041?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. 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.
    2. 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.
    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. S. Bhaskaran & Raja Marappan & B. Santhi, 2020. "Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets," Mathematics, MDPI, vol. 8(7), pages 1-27, July.

    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 & 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.
    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. 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.
    4. Zhenghui Sha & Yun Huang & Jiawei Sophia Fu & Mingxian Wang & Yan Fu & Noshir Contractor & Wei Chen, 2018. "A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations," Complexity, Hindawi, vol. 2018, pages 1-14, January.
    5. 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).
    6. Lee, Yan-Li & Zhou, Tao & Yang, Kexin & Du, Yajun & Pan, Liming, 2023. "Personalized recommender systems based on social relationships and historical behaviors," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    7. Qian, Fulan & Zhao, Shu & Tang, Jie & Zhang, Yanping, 2016. "SoRS: Social recommendation using global rating reputation and local rating similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 61-72.
    8. 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).
    9. Ma, Tinghuai & Suo, Xiafei & Zhou, Jinjuan & Tang, Meili & Guan, Donghai & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2016. "Augmenting matrix factorization technique with the combination of tags and genres," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 101-116.
    10. 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.
    11. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Mingyang Zhou & Yi-Cheng Zhang, 2019. "Enhancing countries' fitness with recommender systems on the international trade network," Papers 1904.02412, arXiv.org.
    12. Ramezani, Mohsen & Yaghmaee, Farzin, 2016. "A novel video recommendation system based on efficient retrieval of human actions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 607-623.
    13. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    14. 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).
    15. Hao Liao & Xiao-Min Huang & Xing-Tong Wu & Ming-Kai Liu & Alexandre Vidmer & Ming-Yang Zhou & Yi-Cheng Zhang, 2018. "Enhancing Countries’ Fitness with Recommender Systems on the International Trade Network," Complexity, Hindawi, vol. 2018, pages 1-12, October.
    16. Zhu, Bei & Yeung, Chi Ho & Liem, Rhea Patricia, 2021. "The impact of common neighbor algorithm on individual friend choices and online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(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:483:y:2017:i:c:p:273-279. 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.