Research Article
Movie Recommendation Using Content-Based and Collaborative Filtering Approach
@INPROCEEDINGS{10.1007/978-3-031-35078-8_37, author={Anjali Jha and Nidhi Agarwal and Devendra K. Tayal and Vrinda Abrol and Deepakshi and Yashica Garg and Anushka }, title={Movie Recommendation Using Content-Based and Collaborative Filtering Approach}, proceedings={Intelligent Systems and Machine Learning. First EAI International Conference, ICISML 2022, Hyderabad, India, December 16-17, 2022, Proceedings, Part I}, proceedings_a={ICISML}, year={2023}, month={7}, keywords={Pandas Python Recommendation System Content-Based Filtering Collaborative filtering Movie Recommendation}, doi={10.1007/978-3-031-35078-8_37} }- Anjali Jha
Nidhi Agarwal
Devendra K. Tayal
Vrinda Abrol
Deepakshi
Yashica Garg
Anushka
Year: 2023
Movie Recommendation Using Content-Based and Collaborative Filtering Approach
ICISML
Springer
DOI: 10.1007/978-3-031-35078-8_37
Abstract
A recommender system is one that tries to anticipate or filter preferences based on the user’s selections. Films, music, journalism, publications, scientific papers and items in general all make use of recommender systems. We’re building a recommendation system with Python and Pandas in this model. Utilizing an approach called content-based filtering, which is based on the information provided about the items, the system makes suggestions for movies that are comparable to those that a user has previously enjoyed. The information we know about the items and the user’s previous choices are used to calculate this similarity. Combining collaborative filtering and content-based filtering is used to overcome the shortcomings of these two types of filtering generally so that a better recommendation system may be created.
