  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:
https://github.com/RUCAIBox/UniSRec.
