A Semantic Approach to Solve Scalability, Data Sparsity and Cold-Start Problems in Movie Recommendation Systems
Keywords:
Scalability, Data sparsity, Cold-start problem, Singular Value DecompositionAbstract
Recommender systems play a vital role in providing users with personalized information and enhancing their browsing experiences. However, despite the advancements in collaborative filtering techniques, several challenges persist in movie recommendation systems, including the cold start problem, scalability limitations, and data sparsity. The cold start problem arises when there is insufficient data to establish connections between users and items, resulting in inaccurate recommendations. Data sparsity further complicates the issue by making it difficult to identify reliable similar users due to the limited ratings provided by active users. Scalability poses yet another challenge, as real-time environments with a high number of users and extensive data processing requirements struggle to deliver efficient recommendations. To address these issues, this paper proposes a semantic approach that leverages singular value decomposition (SVD), a matrix factorization technique. By applying SVD, the system reduces the dimensionality of the data, overcoming the limitations of the cold start problem, scalability, and data sparsity. Experimental results demonstrate the effectiveness of the proposed system, showcasing improved recommendation accuracy and the ability to generate reliable suggestions even in situations with limited data. Moreover, the system showcases scalability by efficiently processing large volumes of data in real-time, ensuring seamless user experiences. Overall, this semantic approach offers a comprehensive solution to tackle the challenges of scalability, data sparsity, and the cold start problem in movie recommendation systems, potentially enhancing user satisfaction and recommendation quality.
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Dina Fitria Murad; Rosilah Hassan; Bambang Dwi Wijanarko; Riyan Leandros; Silvia Ayunda Murad, 2022 “Evaluation of Hybrid Collaborative Filtering Approach with Context-Sensitive Recommendation System” arXiv:10.1109
Habeebunissa Begum, G.S.S Rao (2017). Associating Social Media to e-Merchandise - A Cold Start Commodity Recommendation. International Journal of Computer Engineering In Research Trends.4(10),378-382.
Mate Pocs, 2020 “Memory and Model based Collaborative Filtering techniques”
SongJie Gong, HongWu Ye, HengSong Tan, 2009 “Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System ”
Milind M. Sutar. Tanveer I. Bagban (2017). Survey on: Prediction of Rating based on Social Sentiment. International Journal of Computer Engineering In Research Trends.4(11),533-538.
N.Satish Kumar, Sujan Babu Vadde (2015), Typicality Based Content-Boosted Collaborative Filtering Recommendation Framework. International Journal of Computer Engineering In Research Trends.2(1),809-813.
Sonule Prashika Abasaheb, Tanveer I. Bagban (2016). A Survey on Web Page Recommendation and Data Preprocessing. International Journal of Computer Engineering In Research Trends.3(4),204-209.
A.Avinash,.N.Sujatha (2016). Location-aware and Personalized Collaborative Filtering for Web Service Recommendation. International Journal of Computer Engineering In Research Trends.3(5),356-360
Gladys T. Dimatacot , Katherine B. Parangat(2022). Effectiveness of Cooperative Learning On the Academic Performance in Mathematics of Junior High School Students in the Philippines. International Journal of Computer Engineering In Research Trends.9(2),51-58.
Kumar, P. ., Gupta, M. K. ., Rao, C. R. S. ., Bhavsingh, M. ., & Srilakshmi, M. (2023). A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 184–192. https://doi.org/10.17762/ijritcc.v11i3s.6180
Ramana, K. V. ., Muralidhar, A. ., Balusa, B. C. ., Bhavsingh, M., & Majeti, S. . (2023). An Approach for Mining Top-k High Utility Item Sets (HUI). International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 198–203. https://doi.org/10.17762/ijritcc.v11i2s.6045
Yu, K., Zhu, S., Lafferty, J., & Gong, Y. (2009, July). Fast nonparametric matrix factorization for large-scale collaborative filtering. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 211-218).
Salakhutdinov, R., Mnih, A., & Hinton, G. (2007, June). Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning (pp. 791-798).
Wang, H., Wang, N., & Yeung, D. Y. (2015, August). Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1235-1244).
Xue, H. J., Dai, X., Zhang, J., Huang, S., & Chen, J. (2017, August). Deep matrix factorization models for recommender systems. In IJCAI (Vol. 17, pp. 3203-3209).
Zhang, S., Yao, L., & Xu, X. (2017, August). Autosvd++ an efficient hybrid collaborative filtering model via contractive auto-encoders. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 957-960).
Ouyang, Y., Liu, W., Rong, W., et al. (2014). Autoencoder-based collaborative filtering. In Int. Conf. on Neural Information Processing (pp. 284-291). Kuching, Malaysia.
Sedhain, S., Menon, A.K., Sanner, S., et al. (2015). Autorec: autoencoders meet collaborative filtering. In Proc. 24th Int. Conf. on World Wide Web (pp. 111-112). Florence, Italy.
Wu, Y., DuBois, C., Zheng, A.X., et al. (2016). Collaborative denoising auto-encoders for top-n recommender systems. In Proc. of the Ninth ACM Int. Conf. on Web Search and Data Mining (pp. 153-162). San Francisco, California, USA.
Yan, W., Wang, D., Cao, M., et al. (2019). Deep auto encoder model with convolutional text networks for video recommendation. IEEE Access, 7, 40333-40346.
Strub, F., Gaudel, R., Mary, J. (2016). Hybrid recommender system based on autoencoders. In Proc. of the 1st Workshop on Deep Learning for Recommender Systems (pp. 11-16). Boston, MA, USA.
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