Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We promise to release all code & datasets for future research.

翻译：适配器是一种带有少量可调参数的插件式神经网络模块，已成为将预训练模型适配到下游任务的参数高效迁移学习技术，尤其在自然语言处理和计算机视觉领域表现突出。与此同时，直接从原始物品模态特征（如NLP中的文本和CV中的图像）学习推荐模型，能够构建高效且可迁移的推荐系统（称为TransRec）。鉴于此，一个自然的问题是：基于适配器的学习技术能否实现性能良好的参数高效TransRec？为此，我们通过实证研究来解决几个关键子问题。首先，我们探究基于适配器的TransRec是否能够达到与基于标准全参数微调的TransRec相当的性能？这在包含不同物品模态（如文本推荐系统和视觉推荐系统）的推荐场景是否成立？若成立，我们将这些已在NLP和CV任务中被证明有效的现有适配器，在物品推荐设置下进行基准测试。第三，我们仔细研究了基于适配器的TransRec在插入位置和插入方式方面的几个关键因素。最后，我们通过扩大源训练数据规模或缩减目标训练数据规模，考察基于适配器的TransRec的效果。我们的论文为统一化与可迁移推荐这一鲜有研究的推荐场景提供了关键见解和实用指导。我们承诺公开所有代码与数据集，以便未来研究。