SMART MOVIE RECOMMENDER: LEVERAGING COLLABORATIVE FILTERING FOR ENHANCED USER EXPERIENCE

Manya Kamra; Yanvi Joshi; Pranav Banga; Rachna Srivastava

doi:10.29121/granthaalayah.v12.i6.2024.6103

Authors

Manya Kamra Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Yanvi Joshi Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Pranav Banga Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad
Rachna Srivastava Department Of Computer Science & Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/granthaalayah.v12.i6.2024.6103

Keywords:

Collaborative Filtering, Movie Attributes, Personalized, Implemented in Python

Abstract [English]

This project presents a movie recommendation system using collaborative filtering, which predicts user preferences based on the behavior of similar users. Unlike content-based approaches that rely on movie attributes, this method uses past user interactions to generate personalized and diverse suggestions. The system, implemented in Python, processes user IDs, movie ratings, item IDs, and timestamps to compute correlations and similarities between users.
Key advantages of the approach include independence from item metadata, scalability across various content types, and the ability to suggest unexpected yet relevant items. Inspired by real-world systems like Netflix and Amazon, this model aims to improve recommendation accuracy and user satisfaction through data-driven personalization.

Downloads

Download data is not yet available.

References

Adomavicius, G., & Tuzhilin, A. (2005). Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. https://doi.org/10.1109/TKDE.2005.99 DOI: https://doi.org/10.1109/TKDE.2005.99

Adomavicius, G., & Tuzhilin, A. (2011). Context-aware recommender systems. Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3_7 DOI: https://doi.org/10.1145/1864708.1864801

Basu, C., Hirsh, H., & Cohen, W. (1998). Recommendation as classification: Using social and content-based information in recommendation. AAAI/IAAI.

Bennett, J., & Lanning, S. (2007). The Netflix prize. KDD Cup and Workshop.

Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2013.03.012 DOI: https://doi.org/10.1016/j.knosys.2013.03.012

Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction. https://doi.org/10.1023/A:1021240730564 DOI: https://doi.org/10.1023/A:1021240730564

Burke, R. (2007). Hybrid web recommender systems. The Adaptive Web. https://doi.org/10.1007/978-3-540-72079-9_12 DOI: https://doi.org/10.1007/978-3-540-72079-9_12

Celma, Ò. (2010). Music recommendation and discovery in the long tail. Springer. https://doi.org/10.1007/978-3-642-13287-2 DOI: https://doi.org/10.1007/978-3-642-13287-2

Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems. https://doi.org/10.1145/230538.230561 DOI: https://doi.org/10.1145/230538.230561

Harper, F. M., & Konstan, J. A. (2015). The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 5(4), 1-19. https://doi.org/10.1145/2827872

Harper, F. M., & Konstan, J. A. (2015). The MovieLens datasets: History and context. ACM Transactions on Interactive Intelligent Systems. https://doi.org/10.1145/2827872 DOI: https://doi.org/10.1145/2827872

He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. WWW. https://doi.org/10.1145/3038912.3052569 DOI: https://doi.org/10.1145/3038912.3052569

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems. https://doi.org/10.1145/963770.963772 DOI: https://doi.org/10.1145/963770.963772

Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Transactions on Information Systems. https://doi.org/10.1145/963770.963774 DOI: https://doi.org/10.1145/963770.963774

Joachims, T., Swaminathan, A., & de Rijke, M. (2017). Deep learning with logged bandit feedback. ICLR.

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix Factorization Techniques for Recommender Systems. Computer, 42(8), 30-37. https://doi.org/10.1109/MC.2009.263

Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. IEEE Computer. https://doi.org/10.1109/MC.2009.263 DOI: https://doi.org/10.1109/MC.2009.263

Lam, X. N., Vu, T., Le, T. D., & Duong, A. D. (2008). Addressing cold-start problem in recommendation systems. Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication. https://doi.org/10.1145/1352793.1352837 DOI: https://doi.org/10.1145/1352793.1352837

McNee, S. M., Riedl, J., & Konstan, J. A. (2006). Being accurate is not enough: How accuracy metrics have hurt recommender systems. CHI. https://doi.org/10.1145/1125451.1125659 DOI: https://doi.org/10.1145/1125451.1125659

Paterek, A. (2007). Improving regularized singular value decomposition for collaborative filtering. KDD Cup.

Pazzani, M., & Billsus, D. (2007). Content-based recommendation systems. The Adaptive Web. https://doi.org/10.1007/978-3-540-72079-9_10 DOI: https://doi.org/10.1007/978-3-540-72079-9_10

Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. UAI.

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. CSCW. https://doi.org/10.1145/192844.192905 DOI: https://doi.org/10.1145/192844.192905

Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to Recommender Systems Handbook. Springer. https://doi.org/10.1007/978-0-387-85820-3_1 DOI: https://doi.org/10.1007/978-0-387-85820-3

Salton, G., & McGill, M. J. (1983). Introduction to modern information retrieval. McGraw-Hill.

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based Collaborative Filtering Recommendation Algorithms. WWW Conference. https://doi.org/10.1145/371920.372071

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001). Item-based collaborative filtering recommendation algorithms. WWW. https://doi.org/10.1145/371920.372071 DOI: https://doi.org/10.1145/371920.372071

Schein, A. I., Popescul, A., Ungar, L. H., & Pennock, D. M. (2002). Methods and Metrics for Cold-Start Recommendations. SIGIR. https://doi.org/10.1145/564376.564421 DOI: https://doi.org/10.1145/564376.564421

Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3_8 DOI: https://doi.org/10.1007/978-0-387-85820-3_8

Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009. https://doi.org/10.1155/2009/421425

Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence. https://doi.org/10.1155/2009/421425 DOI: https://doi.org/10.1155/2009/421425

Tintarev, N., & Masthoff, J. (2011). Designing and evaluating explanations for recommender systems. Recommender Systems Handbook. https://doi.org/10.1007/978-0-387-85820-3_15 DOI: https://doi.org/10.1007/978-0-387-85820-3_15

Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning-based recommender system: A survey and new perspectives. ACM Computing Surveys. https://doi.org/10.1145/3285029 DOI: https://doi.org/10.1145/3285029