In order to develop effective sequential recommenders, a series of sequence
representation learning (SRL) methods are proposed to model historical user
behaviors. Most existing SRL methods rely on explicit item IDs for developing
the sequence models to better capture user preference. Though effective to some
extent, these methods are difficult to be transferred to new recommendation
scenarios, due to the limitation by explicitly modeling item IDs. To tackle
this issue, we present a novel universal sequence representation learning
approach, named UniSRec. The proposed approach utilizes the associated
description text of items to learn transferable representations across
different recommendation scenarios. For learning universal item
representations, we design a lightweight item encoding architecture based on
parametric whitening and mixture-of-experts enhanced adaptor. For learning
universal sequence representations, we introduce two contrastive pre-training
tasks by sampling multi-domain negatives. With the pre-trained universal
sequence representation model, our approach can be effectively transferred to
new recommendation domains or platforms in a parameter-efficient way, under
either inductive or transductive settings. Extensive experiments conducted on
real-world datasets demonstrate the effectiveness of the proposed approach.
Especially, our approach also leads to a performance improvement in a
cross-platform setting, showing the strong transferability of the proposed
universal SRL method. The code and pre-trained model are available at:
this https URL.

提出了一种名为 UniSRec 的序列表示学习方法，利用与物品相关的描述文本来学习跨不同场景或平台的可转移表示，其中包括轻量级物品编码体系结构和多域负数采样的对比预训练任务。通过预训练的模型将有效地转移到新的推荐领域或平台，在真实世界的数据集上进行的广泛实验表明了该方法的有效性。