Comparative Research on Embedding Methods for Video Knowledge Graph

In the video recommendation scenario, knowledge graphs are usually introduced to supplement the data information between videos to achieve information expansion and solve the problems of data sparsity and user cold start. However, there are few high-quality knowledge graphs available in the field of video recommendation, and there are many schemes based on knowledge graph embedding, which have different effects on recommendation performance and bring difficulties to researchers. Based on the streaming media video website data, this paper constructs knowledge graphs of two typical scenarios (i.e., sparse distribution scenarios and dense distribution scenarios ). Moreover, six state-of-the-art knowledge graph embedding methods are analyzed based on extensive experiments from three aspects: data distribution type, data set segmentation method, and recommended quantity range. Comparing the recommendation effect of knowledge graph embedding methods. The experimental results demonstrate that: in the sparse distribution scenario , the recommendation effect using TransE is the best; in the dense distribution scenario, the recommendation effect using TransE or TranD is the best. It provides a reference for subsequent researchers on how to choose knowledge map embedding methods under specific data distribution.