Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up

Shenzheng Zhang, Qi Tan, Xinzhi Zheng +2

The gap between the randomly initialized item ID embedding and the well-trained warm item ID embedding makes the cold items hard to suit the recommendation system, which is trained…