GoRec: A Generative Cold-start Recommendation Framework

Multimedia-based recommendation models learn user and item preference representation by fusing both the user-item collaborative signals and the multimedia content signals. In real scenarios, cold items appear in the test stage without any user interaction record. How to perform cold item recommendation is challenging as the training items and test items have different data distributions. These hybrid preference representations contained auxiliary collaborative signals, so current solutions designed alignment functions to transfer learned hybrid preference representations to cold items. Despite the effectiveness, we argue that they are still limited as these models relied heavily on the manually carefully designed alignment functions, which are easily influenced by the limited item records and noises in the training data.

To tackle the above limitations, we propose a Generative coldstart Recommendation (GoRec) framework for multimedia-based new item recommendation. Specifically, we design a Conditional Variational AutoEncoder (CVAE) based method that first estimates the underlying distribution of each warm item conditioned on the