Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

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Content-based Filtering for Improving Movie Recommender System

Authors
Xinhua Tian1, *
1New College, University of Toronto, Toronto, Canada
*Corresponding author. Email: xinhua.tian@utoronto.ca
Corresponding Author
Xinhua Tian
Available Online 14 February 2024.
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.

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Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_61
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_61How to use a DOI?
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

risenwbib
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  -