Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation
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 source...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE
2024-03-01
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| Series: | Ho Chi Minh City Open University Journal of Science - Engineering and Technology |
| Subjects: | |
| Online Access: | https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/2927 |
| _version_ | 1827311467717722112 |
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| author | Hong Thi Thu Phan Vuong Luong Nguyen Trinh Quoc Vo Nguyen Ho Trong Pham |
| author_facet | Hong Thi Thu Phan Vuong Luong Nguyen Trinh Quoc Vo Nguyen Ho Trong Pham |
| author_sort | Hong Thi Thu Phan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T20:19:32Z |
| format | Article |
| id | doaj.art-6d6a2a91918247c2a1e2bb92e8dd5914 |
| institution | Directory Open Access Journal |
| issn | 2734-9330 2734-9608 |
| language | English |
| last_indexed | 2024-04-24T20:19:32Z |
| publishDate | 2024-03-01 |
| publisher | HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE |
| record_format | Article |
| series | Ho Chi Minh City Open University Journal of Science - Engineering and Technology |
| spelling | doaj.art-6d6a2a91918247c2a1e2bb92e8dd59142024-03-22T08:56:28ZengHO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCEHo Chi Minh City Open University Journal of Science - Engineering and Technology2734-93302734-96082024-03-01141415110.46223/HCMCOUJS.tech.en.14.1.2927.20242060Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendationHong Thi Thu Phan0Vuong Luong Nguyen1Trinh Quoc Vo2Nguyen Ho Trong Pham3FPT University, Da NangFPT University, Da NangFPT University, Da NangFPT University, Da NangThis 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.https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/2927collaborative filteringk-mean clusteringknowledge-basedmovie similarityrecommendation system |
| spellingShingle | Hong Thi Thu Phan Vuong Luong Nguyen Trinh Quoc Vo Nguyen Ho Trong Pham Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation Ho Chi Minh City Open University Journal of Science - Engineering and Technology collaborative filtering k-mean clustering knowledge-based movie similarity recommendation system |
| title | Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation |
| title_full | Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation |
| title_fullStr | Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation |
| title_full_unstemmed | Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation |
| title_short | Hybrid knowledge-infused collaborative filtering for enhanced movie clustering and recommendation |
| title_sort | hybrid knowledge infused collaborative filtering for enhanced movie clustering and recommendation |
| topic | collaborative filtering k-mean clustering knowledge-based movie similarity recommendation system |
| url | https://journalofscience.ou.edu.vn/index.php/tech-en/article/view/2927 |
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