Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework
The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with so...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10830490/ |
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| author | Mehrdad Maghsoudi Mohammad Hossein Valikhani Mohammad Hossein Zohdi |
| author_facet | Mehrdad Maghsoudi Mohammad Hossein Valikhani Mohammad Hossein Zohdi |
| author_sort | Mehrdad Maghsoudi |
| collection | DOAJ |
| description | The proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with social network analysis to enhance personalization in VOD platforms. The methodology constructs user-item interaction graphs based on viewing patterns and applies centrality measures (degree, closeness, and betweenness) to identify important videos. Modularity-based clustering groups related content, enabling personalized recommendations. The system was evaluated on a documentary-focused VOD platform with 328 users over four months. Results showed significant improvements: a 63% increase in click-through rate (CTR), a 24% increase in view completion rate, and a 17% improvement in user satisfaction. The approach outperformed traditional methods like Naive Bayes and SVM. Future research should explore advanced techniques, such as matrix factorization models, graph neural networks, and hybrid approaches combining content-based and collaborative filtering. Additionally, incorporating temporal models and addressing scalability challenges for large-scale platforms are essential next steps. This study contributes to the state of the art by introducing modularity-based clustering and ego-centric ranking methods to enhance personalization in video recommendations. The findings suggest that integrating network-based features and implicit feedback can significantly improve user engagement, offering a cost-effective solution for VOD platforms to enhance recommendation quality. |
| format | Article |
| id | doaj-art-ffb72e976fe94eac8c44a3a1230a9c36 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ffb72e976fe94eac8c44a3a1230a9c362025-01-14T00:02:25ZengIEEEIEEE Access2169-35362025-01-01135660567810.1109/ACCESS.2025.352687610830490Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated FrameworkMehrdad Maghsoudi0https://orcid.org/0000-0002-1896-1825Mohammad Hossein Valikhani1Mohammad Hossein Zohdi2Department of Industrial and Information Management, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, IranDepartment of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranDepartment of Management, Economics and Progress Engineering, Iran University of Science and Technology, Tehran, IranThe proliferation of video-on-demand (VOD) services has led to a paradox of choice, overwhelming users with vast content libraries and revealing limitations in current recommender systems. This research introduces a novel approach by combining implicit user data, such as viewing percentages, with social network analysis to enhance personalization in VOD platforms. The methodology constructs user-item interaction graphs based on viewing patterns and applies centrality measures (degree, closeness, and betweenness) to identify important videos. Modularity-based clustering groups related content, enabling personalized recommendations. The system was evaluated on a documentary-focused VOD platform with 328 users over four months. Results showed significant improvements: a 63% increase in click-through rate (CTR), a 24% increase in view completion rate, and a 17% improvement in user satisfaction. The approach outperformed traditional methods like Naive Bayes and SVM. Future research should explore advanced techniques, such as matrix factorization models, graph neural networks, and hybrid approaches combining content-based and collaborative filtering. Additionally, incorporating temporal models and addressing scalability challenges for large-scale platforms are essential next steps. This study contributes to the state of the art by introducing modularity-based clustering and ego-centric ranking methods to enhance personalization in video recommendations. The findings suggest that integrating network-based features and implicit feedback can significantly improve user engagement, offering a cost-effective solution for VOD platforms to enhance recommendation quality.https://ieeexplore.ieee.org/document/10830490/Video recommendationsocial network analysisimplicit feedbackmodularity clusteringego-centric ranking |
| spellingShingle | Mehrdad Maghsoudi Mohammad Hossein Valikhani Mohammad Hossein Zohdi Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework IEEE Access Video recommendation social network analysis implicit feedback modularity clustering ego-centric ranking |
| title | Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework |
| title_full | Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework |
| title_fullStr | Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework |
| title_full_unstemmed | Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework |
| title_short | Network-Based Video Recommendation Using Viewing Patterns and Modularity Analysis: An Integrated Framework |
| title_sort | network based video recommendation using viewing patterns and modularity analysis an integrated framework |
| topic | Video recommendation social network analysis implicit feedback modularity clustering ego-centric ranking |
| url | https://ieeexplore.ieee.org/document/10830490/ |
| work_keys_str_mv | AT mehrdadmaghsoudi networkbasedvideorecommendationusingviewingpatternsandmodularityanalysisanintegratedframework AT mohammadhosseinvalikhani networkbasedvideorecommendationusingviewingpatternsandmodularityanalysisanintegratedframework AT mohammadhosseinzohdi networkbasedvideorecommendationusingviewingpatternsandmodularityanalysisanintegratedframework |