IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i13p2962-d1185801.html
   My bibliography  Save this article

A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts

Author

Listed:
  • Yong Eui Kim

    (Law School, Dong-A University, Busan-si 49236, Republic of Korea
    These authors contributed equally to this work.)

  • Sang-Min Choi

    (Department of Computer Science, Gyeongsang National University, Jinju-si 52828, Republic of Korea
    The Research Institute of Natural Science, Gyeongsang National University, Jinju-si 52828, Republic of Korea
    These authors contributed equally to this work.)

  • Dongwoo Lee

    (Manager S/W Development Wellxecon Corp., Seoul 06168, Republic of Korea)

  • Yeong Geon Seo

    (Department of Computer Science, Gyeongsang National University, Jinju-si 52828, Republic of Korea
    Department of AI Convergence Engineering, Gyeongsang National University, Jinju-si 52828, Republic of Korea)

  • Suwon Lee

    (Department of Computer Science, Gyeongsang National University, Jinju-si 52828, Republic of Korea
    The Research Institute of Natural Science, Gyeongsang National University, Jinju-si 52828, Republic of Korea
    Department of AI Convergence Engineering, Gyeongsang National University, Jinju-si 52828, Republic of Korea)

Abstract

Personalized recommender systems are used not only in e-commerce companies but also in various web applications. These systems conventionally use collaborative filtering (CF) and content-based filtering approaches. CF operates using memory-based or model-based methods; both methods use a user-item matrix that considers user preferences as items. This matrix denotes information on user preferences, which refers to the user ratings for items. The model-based method exploits the fact that the input matrix is factorized. CF approaches can effectively provide personalized recommendation results to users; however, cold-start problems arise because both these methods depend on the users’ ratings for items to predict users’ preferences. We proposed an approach to alleviate the cold-start problem along with a methodology for utilizing blockchain that can enhance the reliability of the processes of the recommendations. We attempted to predict an average rating for a new item to alleviate item-side cold-start problems. First, we applied the concept of word2vec, treating each user’s item-selection history as a sentence. Then, we derived genre2Vec based on the skip-gram technique and predicted an average rating for a new item by utilizing the vectors and category ratings. We experimentally demonstrated that our approach could generate more accurate results than conventional CF approaches could. We also designed the processes of the recommendation based on the concept of blockchain addressing the smart contract. Based on our approach, we proposed a system that can secure reliability as well as alleviate the cold-start problems in recommender systems.

Suggested Citation

  • Yong Eui Kim & Sang-Min Choi & Dongwoo Lee & Yeong Geon Seo & Suwon Lee, 2023. "A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2962-:d:1185801
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/13/2962/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/13/2962/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sang-Min Choi & Dongwoo Lee & Kiyoung Jang & Chihyun Park & Suwon Lee, 2023. "Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
    2. 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.
    3. Tarun Kumar Agrawal & Jannis Angelis & Wajid Ali Khilji & Ravi Kalaiarasan & Magnus Wiktorsson, 2023. "Demonstration of a blockchain-based framework using smart contracts for supply chain collaboration," International Journal of Production Research, Taylor & Francis Journals, vol. 61(5), pages 1497-1516, March.
    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. 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.
    2. Li, Man & Wen, Luosheng & Chen, Feiyu, 2021. "A novel Collaborative Filtering recommendation approach based on Soft Co-Clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    3. 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.
    4. Jun Yao & Jianhui Chen, 2023. "A Study on the Characteristics of Middle-aged Chinese Female Users Based on Clothing Needs," Asian Social Science, Canadian Center of Science and Education, vol. 19(4), pages 1-86, August.
    5. 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.
    6. Tae-Yeun Kim & Sung Bum Pan & Sung-Hwan Kim, 2020. "Sentiment Digitization Modeling for Recommendation System," Sustainability, MDPI, vol. 12(12), pages 1-27, June.
    7. 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.
    8. Sang-Min Choi & Dongwoo Lee & Kiyoung Jang & Chihyun Park & Suwon Lee, 2023. "Improving Data Sparsity in Recommender Systems Using Matrix Regeneration with Item Features," Mathematics, MDPI, vol. 11(2), pages 1-26, January.

    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:11:y:2023:i:13:p:2962-:d:1185801. 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.