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

With numerous pieces of information emerging daily and greatly influencing people's lives, large-scale recommendation systems are necessary for timely bridging the users with their desired information. However, the existing widely used embedding-based recommendation systems have a shortcoming in recommending new items because little interaction data is available for training new item ID embedding, which is recognized as item cold start problem. 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 on the data of historical warm items. To alleviate the performance decline of new items recommendation, the distribution of the new item ID embedding should be close to that of the historical warm items. To achieve this goal, we propose an Adversarial Variational Autoencoder Warm-up model (AVAEW) to generate warm-up item ID embedding for cold items. Specifically, we develop a conditional variational autoencoder model to leverage the side information of items for generating the warm-up item ID embedding. Particularly, we introduce an adversarial module to enforce the alignment between warm-up item ID embedding distribution and historical item ID embedding distribution. We demonstrate the effectiveness and compatibility of the proposed method by extensive offline experiments on public datasets and online A/B tests on a real-world large-scale news recommendation platform.