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.

翻译：从稀疏的用户交互中学习有效的推荐模型，是开发序列推荐方法面临的核心挑战。近期，基于预训练的方法已被提出以应对这一挑战。尽管有前景，但在本文中，我们表明现有方法存在著名的负迁移问题，即从预训练模型适配得到的模型在目标任务上的表现，反而不如从头学习的模型。为解决此问题，我们提出了一种名为ANT的可迁移序列推荐方法。ANT通过以下方式缓解负迁移：1）整合多模态物品信息，包括物品文本、图像和价格，从而更有效地从相关任务（即辅助任务）中学习可迁移知识；2）采用基于再学习的适配策略，在目标任务中更好地捕获任务特定知识。我们在五个目标任务上，将ANT与八种最先进的基线方法进行了评估。实验结果表明，ANT在所有目标任务上均未出现负迁移问题。结果还表明，ANT在目标任务上显著优于基线方法，性能提升高达15.2%。我们的分析凸显了基于再学习的策略在目标任务上相比微调具有更优的有效性。