Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

TL;DR

This paper introduces ANT, a novel method for sequential recommendation that leverages multi-modal item information and re-learning to mitigate negative transfer from pre-training, significantly improving performance on multiple tasks.

Contribution

ANT is the first approach to effectively address negative transfer in sequential recommendation by combining multi-modal data and re-learning adaptation strategies.

Findings

ANT outperforms eight baseline methods on five target tasks.

ANT achieves up to 15.2% performance improvement.

Re-learning strategy is more effective than fine-tuning for transfer learning.

Abstract

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promising, in this paper, we show that existing methods suffer from the notorious negative transfer issue, where the model adapted from the pre-trained model results in worse performance compared to the model learned from scratch in the task of interest (i.e., target task). To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from related tasks (i.e., auxiliary tasks); and 2) better capturing task-specific knowledge in the target task using a re-learning-based adaptation strategy. We evaluate ANT against eight state-of-the-art baseline methods on five target tasks. Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks. The results also demonstrate that ANT substantially outperforms baseline methods in the target tasks with an improvement of as much as 15.2%. Our analysis highlights the superior effectiveness of our re-learning-based strategy compared to fine-tuning on the target tasks.