GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction
This addresses the cold-start issue for e-commerce platforms like Taobao, enabling better recommendation of new videos, though it is incremental as it builds on existing graph-based and feature transfer methods.
The paper tackles the item cold-start problem in click-through rate prediction for short videos on e-commerce platforms by proposing GIFT, a graph-guided feature transfer system that leverages information from warmed-up videos, resulting in a 6.82% lift in CTR on Taobao's homepage.
Short video has witnessed rapid growth in the past few years in e-commerce platforms like Taobao. To ensure the freshness of the content, platforms need to release a large number of new videos every day, making conventional click-through rate (CTR) prediction methods suffer from the item cold-start problem. In this paper, we propose GIFT, an efficient Graph-guIded Feature Transfer system, to fully take advantages of the rich information of warmed-up videos to compensate for the cold-start ones. Specifically, we establish a heterogeneous graph that contains physical and semantic linkages to guide the feature transfer process from warmed-up video to cold-start videos. The physical linkages represent explicit relationships, while the semantic linkages measure the proximity of multi-modal representations of two videos. We elaborately design the feature transfer function to make aware of different types of transferred features (e.g., id representations and historical statistics) from different metapaths on the graph. We conduct extensive experiments on a large real-world dataset, and the results show that our GIFT system outperforms SOTA methods significantly and brings a 6.82% lift on CTR in the homepage of Taobao App.