Overview of Content-Based Click-Through Rate Prediction Challenge for Video Recommendation
Peng Wang, Yunsheng Jiang, Chunxu Xu, Xiaohui Xie · 2019
Content cold-start is a core problem in recommendation field, by which service providers can mine the potential profit from content that has not yet been discovered by most users, and provide more accurate personalized service to their users. In video recommendation, video and audio features should cover enough semantic information in the purpose of recommendation, thus should take an non-negligible role for content cold-start. This paper summarizes the Content Based Video Relevance Prediction Challenge held by Hulu, a top online streaming video platform in US, in ACM Multimedia conference 2019. The challenge is a content-based CTR prediction task for video recommendation, where millions of user interaction data and thousands of video features are released for research purpose on related topics.