Content-Based Movie Recommendation System: An Enhanced Approach to Personalized Movie Recommendations
DOI:
https://doi.org/10.55524/ijircst.2023.11.3.12Keywords:
Content-based recommendation system, Movie recommendation, Text vectorization, Cosine similarity, Personalized recommendations, Bag of words, Semantic relationshipsAbstract
With the exponential growth of digital media platforms and the vast amount of available movie content, users are often overwhelmed when selecting movies that match their preferences. Recommender systems have emerged as an effective solution to assist users in discovering relevant and enjoyable movies. Among these systems, content-based recommendation approaches have gained popularity due to their ability to recommend items based on the content characteristics of movies, such as genres, actors, directors, and plot summaries. The first stage of our system involves the collection and preprocessing of movie metadata from various sources, including genres, actors, directors, and plot summaries. Feature extraction techniques are applied to transform the textual information into meaningful representations that capture the essential characteristics of each movie. Next, a content-based filtering algorithm is employed to compute similarity scores between the user's movie preferences and the extracted features of the available movies. The proposed approach contributes to the advancement of movie recommendation systems and has the potential to enhance user engagement and satisfaction in movie selection.
Downloads
References
Sonboli, N. (2022). Controlling the Fairness/Accuracy Tradeoff in Recommender Systems (Doctoral dissertation, University of Colorado at Boulder).
Singh, R. H., Maurya, S., Tripathi, T., Narula, T., & Srivastav, G. (2020). Movie recommendation system using cosine similarity and KNN. International Journal of Engineering and Advanced Technology, 9(5), 556-559.
“Movie Recommendations,” Devpost, Mar. 30, 2019. https://devpost.com/software/an-idea (accessed May 21, 2023).
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. The adaptive web: methods and strategies of web personalization, 325-341.
Chen, Q., & Aickelin, U. (2008). Movie recommendation systems using an artificial immune system. arXiv preprint arXiv:0801.4287.
Adomavicius, G., & Tuzhilin, A. (2010). Context-aware recommender systems. In Recommender systems handbook (pp. 217-253). Boston, MA: Springer US.
Liu, F., & Lee, H. J. (2010). Use of social network information to enhance collaborative filtering performance. Expert systems with applications, 37(7), 4772- 4778.
Wang, Z., Zhang, Y., Chen, H., Li, Z., & Xia, F. (2018, April). Deep user modeling for content-based event recommendation in event-based social networks. In IEEE INFOCOM 2018- IEEE Conference on Computer Communications (pp. 1304- 1312). IEEE.
Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings.
Aggarwal, C. C., & Aggarwal, C. C. (2016). Content-based recommender systems. Recommender systems: The textbook, 139-166.
Bhattacharya, S., & Ankit, L. (2019). Movie recommendation system using bag of words and scikit-learn. Int J Eng Appl Sci Technol, 4, 526-528.
Khatter, H., Goel, N., Gupta, N., & Gulati, M. (2021, September). Movie recommendation system using cosine similarity with sentiment analysis. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 597-603). IEEE.
Rudkowsky, E., Haselmayer, M., Wastian, M., Jenny, M., Emrich, Š., & Sedlmair, M. (2018). More than bags of words: Sentiment analysis with word embeddings. Communication Methods and Measures, 12(2-3), 140-157.
Deho, B. O., Agangiba, A. W., Aryeh, L. F., & Ansah, A. J. (2018, August). Sentiment analysis with word embedding. In 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST) (pp. 1-4). IEEE.
Downloads
Published
Issue
Section
How to Cite
