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Comparative study of recommender system approaches and movie recommendation using collaborative filtering

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  • Taushif Anwar

    (Pondicherry University)

  • V. Uma

    (Pondicherry University)

Abstract

The increasing demand for personalized information has resulted in the development of the Recommender System (RS). RS has been widely utilized and broadly studied to suggest the interests of users and make an appropriate recommendation. This paper gives an overview of several types of recommendation approaches based on user preferences, ratings, domain knowledge, users demographic data, users context and also lists the advantages and disadvantages of each RS approach. In this paper, we also proposed the movie recommendation based on collaborative filtering and singular value decomposition plus-plus (SVD++). The proposed approach is compared with well-known machine learning approaches namely k nearest neighbor (K-NN), singular value decomposition (SVD) and Co-clustering. The proposed approach is experimentally verified using MovieLens 100 K datasets and error of the RS is measured using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The result shows that the proposed approach gives a lesser error rate with RMSE (0.9201) and MAE (0.7219). This approach also overcomes cold-start, data sparsity problems and provides them relevant items and services.

Suggested Citation

  • Taushif Anwar & V. Uma, 2021. "Comparative study of recommender system approaches and movie recommendation using collaborative filtering," 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. 12(3), pages 426-436, June.
    • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:3:d:10.1007_s13198-021-01087-x
    DOI: 10.1007/s13198-021-01087-x
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    References listed on IDEAS

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    1. Adithya Thaduri & Uday Kumar & Ajit Kumar Verma, 2017. "Computational intelligence framework for context-aware decision making," 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. 8(4), pages 2146-2157, December.
    2. Hosseinzadeh Aghdam, Mehdi, 2019. "Context-aware recommender systems using hierarchical hidden Markov model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 89-98.
    3. 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.
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    Cited by:

    1. Rakhi Saxena & Sharanjit Kaur & Harita Ahuja & Sunita Narang, 2024. "Leveraging item attribute popularity for group recommendation," 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. 15(6), pages 2645-2655, June.

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