Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation

Hong Thi Thu Phan (), Vuong Luong Nguyen, Trinh Quoc Vo and Nguyen Ho Trong Pham
Additional contact information
Hong Thi Thu Phan: FPT University, Da Nang, Vietnam
Vuong Luong Nguyen: FPT University, Da Nang, Vietnam
Trinh Quoc Vo: FPT University, Da Nang, Vietnam
Nguyen Ho Trong Pham: FPT University, Da Nang, Vietnam

HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, 2024, vol. 14, issue 1, 41-51

Abstract: This article proposes an enhanced knowledge-based collaborative filtering model for movie recommendation services to address the limitations of collaborative filtering in capturing the diverse preferences and specific characteristics of movies. The proposed model integrates external knowledge sources, such as movie plots and reviews, to enrich the recommendation process. By leveraging this additional information, the model can better understand movies’ unique features and attributes, improving recommendation accuracy and relevance. The knowledge-based features are extracted and incorporated into the collaborative filtering framework, enhancing the model’s ability to match user preferences with movie characteristics. Experiments are conducted using the MovieLens dataset to evaluate the proposed model. The MAE and RMSE metrics are employed to assess the quality of recommendations. Comparative analyses are conducted against various baseline approaches, including popularity-based, CF-based, content-based, and hybrid recommendation models. The experimental results demonstrate the effectiveness of the proposed knowledge-based collaborative filtering model. The proposed model consistently outperforms the baselines, providing more accurate and personalized recommendations.

Keywords: collaborative filtering; k-mean clustering; knowledge-based; movie similarity; recommendation system (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/2927/2033 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bjw:techen:v:14:y:2024:i:1:p:41-51

DOI: 10.46223/HCMCOUJS.tech.en.14.1.2927.2024

Access Statistics for this article

HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY is currently edited by Nguyen Thuan

More articles in HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY from HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY
Bibliographic data for series maintained by Vu Tuan Truong ().