Multi-Task Recommendations with Reinforcement Learning

TL;DR

This paper introduces RMTL, a reinforcement learning-enhanced multi-task learning framework for recommender systems that leverages session-wise interactions and dynamic loss weighting to improve recommendation accuracy.

Contribution

The paper proposes a novel RL-based MTL framework, RMTL, that constructs session-wise environments and uses actor-critic networks for adaptive loss balancing in recommendation models.

Findings

RMTL achieves higher AUC than state-of-the-art models.

RMTL is compatible with various MTL recommendation models.

Experiments validate RMTL's effectiveness and transferability.

Abstract

In recent years, Multi-task Learning (MTL) has yielded immense success in Recommender System (RS) applications. However, current MTL-based recommendation models tend to disregard the session-wise patterns of user-item interactions because they are predominantly constructed based on item-wise datasets. Moreover, balancing multiple objectives has always been a challenge in this field, which is typically avoided via linear estimations in existing works. To address these issues, in this paper, we propose a Reinforcement Learning (RL) enhanced MTL framework, namely RMTL, to combine the losses of different recommendation tasks using dynamic weights. To be specific, the RMTL structure can address the two aforementioned issues by (i) constructing an MTL environment from session-wise interactions and (ii) training multi-task actor-critic network structure, which is compatible with most existing MTL-based recommendation models, and (iii) optimizing and fine-tuning the MTL loss function using the weights generated by critic networks. Experiments on two real-world public datasets demonstrate the effectiveness of RMTL with a higher AUC against state-of-the-art MTL-based recommendation models. Additionally, we evaluate and validate RMTL's compatibility and transferability across various MTL models.