Towards Universal Sequence Representation Learning for Recommender Systems (2206.05941v1)

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.

Analyzing Universal Sequence Representation Learning in Recommender Systems

The paper "Towards Universal Sequence Representation Learning for Recommender Systems" introduces the concept of transferring sequence learning methods across diverse recommendation domains, leveraging universal sequence representation. Historically, sequence representation learning (SRL) in recommender systems models user preferences using explicit item IDs. However, the transferability of these models across different domains is inhibited due to their reliance on explicit IDs. The proposed solution, the UniSRec model, overcomes this limitation by generalizing sequence representation through the use of item description text, which inherently contains domain-agnostic semantic information.

Methodology and Innovations

1. Universal Item Representation: The UniSRec model departs from traditional methods by not using explicit item IDs and instead utilizes the associated description text of items. The essence of this transformation lies in learning universal textual representations using a lightweight item encoding framework, incorporating parametric whitening and mixture-of-experts (MoE) enhanced adaptors. These mechanisms help in deriving isotropic semantic item representations, thereby facilitating domain fusion and adaptation, essential for effective cross-domain knowledge transfer.
2. Universal Sequence Representation: The model introduces two contrastive pre-training tasks—sequence-item contrast and sequence-sequence contrast—to learn generalized sequence behaviors. These tasks involve sampling negatives from multiple domains to enhance semantic fusion across varied user interaction patterns. Such pre-training allows the model to capture domain-invariant sequential patterns, making it more adaptable to new recommendation settings including cross-domain and cross-platform settings.
3. Parameter-Efficient Transfer: A notable feature of UniSRec is its ability to efficiently transfer learning across domains with minimal tuning. Utilizing the MoE-enhanced adaptor, the model learns to maintain core architectures fixed, while selectively adapting lightweight parameters to incorporate new domain features. This approach is both computationally efficient and effective in maintaining the generalization ability of the model.

Empirical Validation

The model's robustness was validated through rigorous experiments on diverse datasets, demonstrating substantial improvements over existing SRL methods when transferred to new domains. Particularly in cross-platform evaluations, UniSRec demonstrated significant performance gain, indicative of its strong transfer potential even when no overlapping users or items exist between the source and target domains. These results underscore the model's capability to generalize user behavior representation effectively, providing a consistent advantage over traditional item ID-centered models.

Implications and Future Directions

The proposed methodology marks a significant progression in developing sequence recommendation systems that are not constrained by domain specificity. By leveraging item text, which offers a rich and consistent source of semantic information, the approach not only enhances transferability but also addresses the cold-start problem commonly faced in recommendation systems. The parametric-efficient fine-tuning strategy ensures the model remains adaptable to dynamic recommendation scenarios without the need for extensive retraining.

Looking ahead, the integration of additional modalities such as visual and audio content into the representation learning framework could further enhance the model's applicability across various media-rich domains. Additionally, extending this approach to support real-time adaptation and lifelong learning would provide further utility in constantly evolving user preference environments.

In conclusion, the UniSRec model presents a compelling case for adopting universal sequence representation in recommender systems, setting the stage for future research in domain-agnostic recommendation systems that are both effective and scalable.