Goal-Conditioned Supervised Learning for Multi-Objective Recommendation

TL;DR

This paper presents MOGCSL, a framework for multi-objective goal-conditioned supervised learning that improves multi-objective recommendation by filtering noise and modeling high-achievable goals, demonstrated on real-world datasets.

Contribution

MOGCSL extends GCSL to multi-objective scenarios with a novel goal-selection algorithm, eliminating complex architectures and effectively handling noisy data in recommender systems.

Findings

MOGCSL outperforms baseline methods on real-world recommendation datasets.

It effectively filters out uninformative or noisy training instances.

The framework demonstrates robustness and scalability in multi-objective recommendation tasks.

Abstract

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 more complex optimization problem, particularly when navigating potential conflicts between 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 effectively filters out uninformative or noisy instances that fail to achieve desirable long-term rewards across multiple objectives. We also introduces a novel goal-selection algorithm for MOGCSL to model and identify "high" achievable goals for inference. While MOGCSL is quite general, 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 on real-world recommendation datasets. Also, analysis and experiments are included to explain its strength in discounting the noisier portions of training data in recommender systems with multiple objectives.