Towards Universal Sequence Representation Learning for Recommender Systems

TL;DR

This paper introduces UniSRec, a universal sequence representation learning method for recommender systems that leverages item description texts and contrastive pre-training to enable effective transfer across different domains and platforms.

Contribution

The paper proposes a novel universal SRL approach using description texts and contrastive learning, improving transferability and efficiency in recommendation scenarios.

Findings

Effective transfer to new domains demonstrated

Performance gains in cross-platform recommendations

Universal representations outperform ID-based methods

Abstract

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.