Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing modern sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. The key idea behind these methods is to learn transferable knowledge from related tasks (i.e., auxiliary tasks) via pre-training and adapt the knowledge to the task of interest (i.e., target task) to mitigate its data sparsity, thereby enabling more accurate recommendations. 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 target task. To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. Compared to existing methods, ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from 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 five tasks. The results also demonstrate that ANT substantially outperforms the state-of-the-art baseline methods in five target tasks with an improvement of as much as 15.2%. Our analysis highlights the utility of item texts, images and prices together for sequential recommendation. It also demonstrates that our re-learning-based strategy is more effective than fine-tuning on all five target tasks.

翻译：从稀疏的用户交互中学习有效的推荐模型是发展现代序列推荐方法的基本挑战。近期，基于预训练的方法被开发出来应对这一挑战。这些方法的核心思想是通过预训练从相关任务（即辅助任务）中学习可迁移知识，并将这些知识适应到感兴趣的任务（即目标任务），以缓解其数据稀疏性问题，从而实现更准确的推荐。尽管这些方法前景广阔，但本文表明，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中的性能比从头学习的模型更差。为解决这一问题，我们提出了一种方法，记为ANT，用于可迁移的序列推荐。与现有方法相比，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，以有效学习来自辅助任务的更可迁移的知识；2）使用基于再学习的自适应策略，更好地捕获目标任务中的任务特定知识。我们在五个目标任务上对ANT与八种最先进的基线方法进行了评估。实验结果表明，ANT在五个任务中均未遭受负迁移问题。结果还表明，ANT在五个目标任务中显著优于最先进的基线方法，提升幅度高达15.2%。我们的分析强调了物品文本、图像和价格在序列推荐中的联合效用，并证明基于再学习的策略在所有五个目标任务上比微调更有效。