This study proposes a novel video recommendation approach that leverages implicit user feedback in the form of viewing percentages and social network analysis techniques. By constructing a video similarity network based on user viewing patterns and computing centrality measures, the methodology identifies important and well-connected videos. Modularity analysis is then used to cluster closely related videos, forming the basis for personalized recommendations. For each user, candidate videos are selected from the cluster containing their preferred items and ranked using an ego-centric index that measures proximity to the user's likes and dislikes. The proposed approach was evaluated on real user data from an Asian video-on-demand platform. Offline experiments demonstrated improved accuracy compared to conventional methods such as Naive Bayes, SVM, decision trees, and nearest neighbor algorithms. An online user study further validated the effectiveness of the recommendations, with significant increases observed in click-through rate, view completion rate, and user satisfaction scores relative to the platform's existing system. These results underscore the value of incorporating implicit feedback and social network analysis for video recommendations. The key contributions of this research include a novel video recommendation framework that integrates implicit user data and social network analysis, the use of centrality measures and modularity-based clustering, an ego-centric ranking approach, and rigorous offline and online evaluation demonstrating superior performance compared to existing techniques. This study opens new avenues for enhancing video recommendations and user engagement in VOD platforms.

翻译：本研究提出一种新型视频推荐方法，通过利用用户观看百分比形式的隐式反馈与社交网络分析技术。通过构建基于用户观看模式的视频相似性网络并计算中心性度量，该方法能识别重要且关联紧密的视频。进一步采用模块度分析对高度相关的视频进行聚类，为个性化推荐奠定基础。针对每位用户，从包含其偏好视频的聚类中筛选候选视频，并采用衡量用户喜好与厌恶视频接近度的自我中心指标进行排序。该方法基于亚洲视频点播平台真实用户数据开展评估。离线实验表明，与朴素贝叶斯、支持向量机、决策树及最近邻算法等传统方法相比，本方法具有更优的准确性。在线用户研究进一步验证了推荐效果：相较于平台现有系统，点击率、完整观看率及用户满意度评分均显著提升。这些结果凸显了将隐式反馈与社交网络分析整合至视频推荐领域的价值。本研究主要贡献包括：融合隐式用户数据与社交网络分析的新型视频推荐框架、中心性度量与模块度聚类方法的应用、自我中心排序策略，以及通过严格的离线与在线评估证明该方法在性能上优于现有技术。本研究为提升视频点播平台的推荐质量与用户参与度开辟了新路径。