Hybrid Movie Recommendation System with Content-Based and Memory-Based Collaborative Filtering based on Deep Neural Network
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Abstract
Recommendation systems assist users in filtering qualified data from vast datasets. Content-based and collaborative filtering techniques are the most widely used among the various recommendation models. In recent years, neural network algorithms have been employed to tackle recommendation problems. How- ever, while these methods reduce the error between true and predicted values, personalized recommenda- tions remain relatively lacking. To address this, we propose a movie recommendation system based on deep neural networks and user vocabulary preference features to alleviate cold start and sparsity issues, re- duce prediction errors, and improve recommendation efficiency. We evaluate our model using hit rate (HR) and average reciprocal hit rank (ARHR) as indica- tors, achieving an HR of 0.76 and an ARHR of 0.38. The robustness of our model is demonstrated through comparisons with other studies.
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References
J. Ni, Y. Cai, G. Tang, and Y. Xie, “Collaborative filtering recommendation algorithm based on tfidf and user characteristics,” Applied Sciences, vol. 11, no. 20, p. 9554, 2021.
M. Mngomezulu and R. Ajoodha, “A contentbased collaborative filtering movie recommendation system using keywords extractions,” in 2022 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2022, pp. 1–6.
Priyati, A. D. Laksito, and H. Sismoro, “The comparison study of matrix factorization on collaborative filtering recommender system,” in 2022 5th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2022, pp.177–182.
S. Reddy, S. Nalluri, S. Kunisetti, S. Ashok, and B. Venkatesh, “Content-based movie recommendation system using genre correlation,” in Smart Intelligent Computing and Applications: Hybrid Movie Recommendation System with Content-Based and Memory-Based Collaborative Filtering based on Deep Neural Network9 Proceedings of the Second International Conference on SCI 2018, Volume 2. Springer, 2019, pp. 391–397.
K. A. Reshak, B. N. Dhannoon, and Z. N. Sultani, “Hybrid recommender system based on matrix factorization,” in AIP Conference Proceedings, vol. 2457, no. 1. AIP Publishing, 2023.
P. Thakkar, K. Varma, V. Ukani, S. Mankad, and S. Tanwar, “Combining user-based and itembased collaborative filtering using machine learning,” in Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018,
Volume 2. Springer, 2019, pp. 173– 180.
K. Cosh, “Current research themes in software engineering: An application of text mining,” in 2016 8th International Conference on Knowledge and Smart Technology (KST). IEEE, 2016, pp. 125–129.
M. R. Hasan and J. Ferdous, “Dominance of ai and machine learning techniques in hybrid movie recommendation system applying textto-number conversion and cosine similarity approaches,” Journal of Computer Science and Technology Studies, vol. 6, no. 1, pp. 94–102, 2024.
H. Kumaar, S. Srikumaran, S. Veni et al., “Contentbased movie recommender system using keywords and plot overview,” in 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). IEEE, 2022, pp. 49–53.
S. Airen and J. Agrawal, “Movie recommender system using k-nearest neighbors variants,” NationalAcademy Science Letters, vol. 45, no. 1, pp. 75–82, 2022.
M. F. Aljunid and M. Dh, “An efficient deep learning approach for collaborative filtering recommender system,” Procedia Computer Science, vol. 171, pp. 829–836, 2020.
M. Moradi and J. Hamidzadeh, “Ensemble-based top-k recommender system considering incomplete data,” Journal of AI and Data Mining, vol. 7, no. 3, pp. 393–402, 2019.
Jadon and A. Patil, “A comprehensive survey of evaluation techniques for recommendation systems,” arXiv preprint arXiv:2312.16015, 2023.
S. Gong, “A collaborative filtering recommendation
algorithm based on user clustering and item clustering.” J. Softw., vol. 5, no. 7, pp. 745–752, 2010.
Priyati, A. D. Laksito, and H. Sismoro, “The comparison study of matrix factorization on collaborative filtering recommender system,” in 2022 5th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2022, pp.
–182.
L. Sang, M. Xu, S. Qian, and X. Wu, “Knowledge graph enhanced neural collaborative recommendation,” Expert systems with applications, vol. 164, p. 113992, 2021.
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “Bpr: Bayesian personalized ranking from implicit feedback,” arXiv preprint arXiv:1205.2618, 2012.
Q.-T. Truong, A. Salah, and H. W. Lauw, “Bilateral variational autoencoder for collaborative filtering,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021, pp. 292–300.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, 2020, pp. 639– 648.