IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2657-d660838.html
   My bibliography  Save this article

Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation

Author

Listed:
  • Chunsheng Cui

    (College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, China)

  • Jielu Li

    (College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450046, China)

  • Zhenchun Zang

    (School of Mathematics and Statistics, Zhoukou Normal University, Zhoukou 466001, China)

Abstract

The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize the historical ratings embedded in the recommendation system and studies the data processing problem of the sparse matrix under the condition of a hesitant fuzzy set. The key is to transform the similarity problem of products in the sparse matrix into the similarity problem of two hesitant fuzzy sets by data conversion, data processing, and data complement. This paper further considers the influence of the difference of user ratings on the recommendation results and obtains a user’s recommendation list. On the one hand, the proposed method effectively solves the matrix in the recommendation system; on the other hand, it provides a feasible method for calculating similarity in the recommendation system.

Suggested Citation

  • Chunsheng Cui & Jielu Li & Zhenchun Zang, 2021. "Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation," Mathematics, MDPI, vol. 9(21), pages 1-13, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2657-:d:660838
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2657/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/21/2657/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heng-Ru Zhang & Fan Min & Xu He & Yuan-Yuan Xu, 2015. "A Hybrid Recommender System Based on User-Recommender Interaction," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, February.
    2. Yan Guo & Chengxin Yin & Mingfu Li & Xiaoting Ren & Ping Liu, 2018. "Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business," Sustainability, MDPI, vol. 10(1), pages 1-13, January.
    Full references (including those not matched with items on IDEAS)

    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. Paulo Rita & Ricardo F. Ramos, 2022. "Global Research Trends in Consumer Behavior and Sustainability in E-Commerce: A Bibliometric Analysis of the Knowledge Structure," Sustainability, MDPI, vol. 14(15), pages 1-20, August.
    2. Mingwei Sun & Katarzyna Grondys & Nazim Hajiyev & Pavel Zhukov, 2021. "Improving the E-Commerce Business Model in a Sustainable Environment," Sustainability, MDPI, vol. 13(22), pages 1-22, November.
    3. Bo Wang & Feiyue Ye & Jialu Xu, 2018. "A Personalized Recommendation Algorithm Based on the User’s Implicit Feedback in E-Commerce," Future Internet, MDPI, vol. 10(12), pages 1-13, November.
    4. Jaekyeong Kim & Ilyoung Choi & Qinglong Li, 2021. "Customer Satisfaction of Recommender System: Examining Accuracy and Diversity in Several Types of Recommendation Approaches," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    5. Shili Mohamed & Kaouthar Sethom & Abdallah Namoun & Ali Tufail & Ki-Hyung Kim & Hani Almoamari, 2022. "Customer Profiling Using Internet of Things Based Recommendations," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    6. Huosong Xia & Xiang Wei & Wuyue An & Zuopeng Justin Zhang & Zelin Sun, 2021. "Design of electronic-commerce recommendation systems based on outlier mining," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 295-311, June.
    7. Hyunwoo Hwangbo & Yangsok Kim, 2019. "Session-Based Recommender System for Sustainable Digital Marketing," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    8. Jaeho Jeong & Dongeon Kim & Xinzhe Li & Qinglong Li & Ilyoung Choi & Jaekyeong Kim, 2022. "An Empirical Investigation of Personalized Recommendation and Reward Effect on Customer Behavior: A Stimulus–Organism–Response (SOR) Model Perspective," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    9. Farah Tawfiq Abdul Hussien & Abdul Monem S. Rahma & Hala B. Abdulwahab, 2021. "An E-Commerce Recommendation System Based on Dynamic Analysis of Customer Behavior," Sustainability, MDPI, vol. 13(19), pages 1-21, September.
    10. Raheleh Hassannia & Ali Vatankhah Barenji & Zhi Li & Habib Alipour, 2019. "Web-Based Recommendation System for Smart Tourism: Multiagent Technology," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
    11. Sandipan Sahu & Raghvendra Kumar & Pathan MohdShafi & Jana Shafi & SeongKi Kim & Muhammad Fazal Ijaz, 2022. "A Hybrid Recommendation System of Upcoming Movies Using Sentiment Analysis of YouTube Trailer Reviews," Mathematics, MDPI, vol. 10(9), pages 1-22, May.
    12. Liang Xiao & Hangxiao Mao & Shu Wang, 2020. "Research on Mobile Marketing Recommendation Method Incorporating Layout Aesthetic Preference for Sustainable m-Commerce," Sustainability, MDPI, vol. 12(6), pages 1-25, March.
    13. Liang Xiao & Qibei Lu & Feipeng Guo, 2020. "Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce," Sustainability, MDPI, vol. 12(7), pages 1-20, April.

    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:gam:jmathe:v:9:y:2021:i:21:p:2657-:d:660838. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.