Knowledge-based Temporal Fusion Network for Interpretable Online Video Popularity Prediction
Shisong Tang, Qing Li, Xiaoteng Ma, Ci Gao, Dingmin Wang, Yong Jiang, Ma Qian, Aoyang Zhang, Hechang Chen
Abstract
Predicting the popularity of online videos has many real-world applications, such as recommendation, precise advertising, and edge caching strategies. Despite many efforts have been dedicated to the online video popularity prediction, there still exist several challenges: (1) The meta-data from online videos is usually sparse and noisy, which makes it difficult to learn a stable and robust representation. (2) The influence of content features and temporal features in different life cycles of online videos is dynamically changing, so it is necessary to build a model that can capture the dynamics. (3) Besides, there is a great need to interpret the predictive behavior of the model to assist administrators of video platforms in the subsequent decision-making.