Enhancing Multi-Task Models For Recommendation with Tensor Trace Norm

Boqi Dai, Kai Ouyang, Jun Yuan, Miaoxin Chen, Xingyu Lu, Weiwen Liu, Rui Zhang, Hai-Tao Zheng

Abstract: Noise is a pervasive issue in recommendation systems, which can stem from user behaviors that do not align with their intentions. As a result, noise reduction has become a prominent area of research in the field of recommendation systems. However, existing noise reduction techniques in recommendation tend to compromise the performance of certain task objectives. Moreover, they require modifying the structure of the model, which introduces inference latency and additional space cost. In this paper, we propose a straightforward yet powerful approach, Multi-layer Tensor trace Norm (MTN), to address noise-related challenges. Our method achieves this by promoting information sharing across different tasks using tensor trace norms. By leveraging norms, MTN effectively reduces noise without modifying the model’s structure or incurring substantial time and space complexities. Extensive experiments on public datasets and generated noisy datasets demonstrate the effectiveness of MTN on several of the most popular multi-task models.