Temporal Factor-Aware Video Affective Analysis and Recommendation for Cyber-Based Social Media

As an important cyber-enabled application, online video recommendation is seeing significant interest from both industry and academia. To effectively recommend video content becomes a popular research topic. However, it has been found that existing recommendation methods based on video affective analysis ignore the temporal factor, leading to poor performance especially when the order of emotion components does affect the recommendation quality. This motivates us to study the feature of emotion fluctuation, which we call Temporal Factor of Emotion (TFE). In this paper, a novel recommendation method based on the Grey Relational Analysis (GRA) is proposed to tackle this problem. GRA preserves the temporal factor of objects during analysis and is suitable for analyzing systems with unknown correlation (a set of independent videos). In our work, first, specific video features are extracted and mapped to the well-known Lovheim emotion-space, through the SVMs (Support Vector Machine). Then, GRA is applied to compute the quantitative relation among videos by using extracted emotions as factors. Finally, a pick-filter pattern and GRA-based recommendation method under the Fisher model are proposed. To evaluate the performance of our method, an online video recommendation system is developed. Experimental results of both user study and parameter evaluation demonstrate that the GRA-based method can improve accuracy of video affective analysis and performance of video recommendation.