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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems

Author 1: Nghia Duong-Trung
Author 2: Quynh Nhut Nguyen
Author 3: Dung Ngoc Le Ha
Author 4: Xuan Son Ha
Author 5: Tan Tai Phan
Author 6: Hiep Xuan Huynh

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110210

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

  • Abstract and Keywords
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Abstract: Abstract—A movie recommender system has been proven to be a convincing implement on carrying out comprehensive and complicated recommendation which helps users find appropriate movies conveniently. It follows a mechanism that a user can be accurately recommended movies based on other similar interests, e.g. collaborative filtering, and the movies themselves, e.g. content-based filtering. Therefore, the systems should come with predeter-mined information either by users or by movies. One interesting research question should be asked: “what if this information is missing or not manually manipulated?” The problem has not been addressed in the literature, especially for the 100K and 1M variations of the MovieLens datasets. This paper exploits the movie recommender system based on movies’ genres and actors/actresses themselves as the input tags or tag interpolation. We apply tag-based filtering and collaborative filtering that can effectively predict a list of movies that is similar to the movie that a user has been watched. Due to not depending on users’ profiles, our approach has eliminated the effect of the cold-start problem. The experiment results obtained on MovieLens datasets indicate that the proposed model may contribute ade-quate performance regarding efficiency and reliability, and thus provide better-personalized movie recommendations. A movie recommender system has been deployed to demonstrate our work. The collected datasets have been published on our Github repository to encourage further reproducibility and improvement.

Keywords: Movielens; movie recommender systems; tag inter-polation; colloborative filtering

Nghia Duong-Trung, Quynh Nhut Nguyen, Dung Ngoc Le Ha, Xuan Son Ha, Tan Tai Phan and Hiep Xuan Huynh, “Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110210

@article{Duong-Trung2020,
title = {Genres and Actors/Actresses as Interpolated Tags for Improving Movie Recommender Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110210},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110210},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Nghia Duong-Trung and Quynh Nhut Nguyen and Dung Ngoc Le Ha and Xuan Son Ha and Tan Tai Phan and Hiep Xuan Huynh}
}


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