Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)

Recommenders provide personalized content recommendations to users. They often suffer from highly skewed long-tail item distributions, with a small fraction of the items receiving most of the user feedback. This hurts model quality especially for the slices without much supervision. Existing work in both academia and industry mainly focuses on re-balancing strategies (e.g., up-sampling and up-weighting), leveraging content features, and transfer learning. However, there still lacks of a deeper understanding of how the long-tail distribution influences the recommendation performance. In this work, we theoretically demonstrate that the prediction of user preference is biased under the long-tail distributions. This bias comes from the discrepancy of both the prior and conditional probabilities between training data and test data. Most existing methods mainly attempt to reduce the bias from the prior perspective, which ignores the discrepancy in the conditional probability. This leads to a severe forgetting issue and results in suboptimal performance. To address the problem, we design a novel Cross Decoupling Network (CDN) to reduce the differences in both prior and conditional probabilities. Specifically, CDN (i) decouples the learning process of memorization and generalization on the item side through a mixture-of-expert structure; (ii) decouples the user samples from different distributions through a regularized bilateral branch network. Finally, a novel adapter is introduced to aggregate the decoupled vectors, and softly shift the training attention to tail items. Extensive experimental results show that CDN significantly outperforms state-of-the-art approaches on popular benchmark datasets, leading to an improvement in HR@50 (hit ratio) of 8.7\% for overall recommendation and 12.4\% for tail items.