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Effective time context based collaborative filtering recommender system inspired by Gower’s coefficient

Author

Listed:
  • Gourav Jain

    (Indian Institute of Technology Roorkee)

  • Tripti Mahara

    (Christ University)

  • S. C.Sharma

    (Indian Institute of Technology Roorkee)

Abstract

The fast growth of Internet technology in recent times has led to a surge in the number of users and amount of information generated. This substantially contributes to the popularity of recommendation systems (RS), which provides personalized recommendations to users based on their interests. A RS assists the user in the decision-making process by suggesting a suitable product from various alternatives. The collaborative filtering (CF) technique of RS is the most prevalent because of its high accuracy in predicting users' interests. The efficacy of this technique mainly depends on the similarity calculation, determined by a similarity measure. However, the traditional and previously developed similarity measures in CF techniques are not able to adequately reveal the change in users' interests; therefore, an efficient measure considering time into context is proposed in this paper. The proposed method and the existing approaches are compared on the MovieLens-100k dataset, showing that the proposed method is more efficient than the comparable methods. Besides this, most of the CF approaches only focus on the historical preference of the users, but in real life, the people's preferences also change over time. Therefore, a time-based recommendation system using the proposed method is also developed in this paper. We implemented various time decay functions, i.e., exponential, convex, linear, power, etc., at various levels of the recommendation process, i.e., similarity computation, rating matrix, and prediction level. Experimental results over three real datasets (MovieLens-100k, Epinions, and Amazon Magazine Subscription) suggest that the power decay function outperforms other existing techniques when applied at the rating matrix level.

Suggested Citation

  • Gourav Jain & Tripti Mahara & S. C.Sharma, 2023. "Effective time context based collaborative filtering recommender system inspired by Gower’s coefficient," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 429-447, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01813-z
    DOI: 10.1007/s13198-022-01813-z
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    References listed on IDEAS

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    1. 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.
    2. Surya Kant & Tripti Mahara, 2018. "Merging user and item based collaborative filtering to alleviate data sparsity," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 173-179, February.
    3. Mubbashir Ayub & Mustansar Ali Ghazanfar & Zahid Mehmood & Tanzila Saba & Riad Alharbey & Asmaa Mahdi Munshi & Mayda Abdullateef Alrige, 2019. "Modeling user rating preference behavior to improve the performance of the collaborative filtering based recommender systems," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-29, August.
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