Content-based Filtering for Improving Movie Recommender System
- DOI
- 10.2991/978-94-6463-370-2_61How to use a DOI?
- Keywords
- Recommendation system; content-based filtering; recommended movie; machine learning; recommendation based on similarity and popularity
- Abstract
People constantly receive personalized information recommendations, and movie recommendation is one of the most recognized applications. Effective algorithms support the analysis of users’ behavior, which helps to improve the rating system. Content-based filtering (CBF) is a major technique in recommender systems that operates on the premise of leveraging the relationship between user preferences and item characteristics to predict items. This paper provides a detailed look about the challenges that this method presents, emphasizing concerns with new users, inherent method limitations, issues with feature sparsity, the challenge of feature extraction, and the potential risk of over-specialization in suggestions. In synthesizing these challenges and innovations, this study highlights the potential of content-based filtering, marking its key role in the ongoing pursuit of personalized content delivery, while suggesting methods for improvement.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Xinhua Tian PY - 2024 DA - 2024/02/14 TI - Content-based Filtering for Improving Movie Recommender System BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 598 EP - 609 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_61 DO - 10.2991/978-94-6463-370-2_61 ID - Tian2024 ER -