Goal-Conditioned Supervised Learning for Multi-Objective Recommendation
Shijun Li, Hilaf Hasson, Jing Hu, Ghosh, Joydeep · arXiv (Cornell University) · 2024
Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performance across diverse objectives. However, this often entails a complex optimization problem to balance the learning of potentially conflicting objectives, leading to solutions with higher memory requirements and computational complexity. This paper introduces a Multi-Objective Goal-Conditioned Supervised Learning (MOGCSL) framework for automatically learning to achieve multiple objectives from offline sequential data. MOGCSL extends the conventional GCSL method to multi-objective scenarios by redefining goals from one-dimensional scalars to multi-dimensional vectors. It benefits from naturally eliminating the need for complex architectures and optimization constraints. Moreover, MOGCSL inherently disentangles uninformative or noisy training instances that fail to achieve desirable long-term rewards across multiple objectives. We also introduce a novel goal-selection algorithm for MOGCSL to model and identify desired and achievable goals for inference. In this paper, we focus on its application to the next action prediction problem in commercial-grade recommender systems. In this context, any viable solution needs to be reasonably scalable and also be robust to large amounts of noisy data that is characteristic of this application space. We show that MOGCSL performs admirably on both counts by extensive experiments. Also, analysis and experiments are included to explain its strength in discounting the noisier portions of training data in recommender systems with multiple objectives.