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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
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

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