Semi-Siamese Network for Content-Based Video Relevance Prediction

The intractable “cold-start” problem often encountered in existed video recommendation systems when a new video is coming with minimal users' feedback since most existing recommendation algorithms heavily rely on the users' implicit feedbacks. This paper presents a novel idea for solving the “cold start” problem by analyzing the video content itself. The Harmonic Sampling is designed in our work for utilizing the rank information automatically during the sampling procedure, and a Semi-Siamese network is proposed for overcoming the asymmetric training samples. The proposed method demonstrated its effectiveness in dealing with “cold-start” problem, achieving superior performance over the Content-based Video Relevance Prediction Dataset.