UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration

TL;DR

Unger introduces a unified code approach for generative recommendation that combines semantic and collaborative knowledge, improving efficiency and effectiveness by joint optimization and knowledge distillation.

Contribution

The paper presents a novel method, UNGER, for integrating semantic and collaborative information into a single code using adaptive learning and knowledge distillation.

Findings

Outperforms existing methods on three benchmark datasets.

Effectively integrates multi-modal knowledge for recommendation.

Reduces computational and storage costs.

Abstract

With the rise of generative paradigms, generative recommendation has garnered increasing attention. The core component is the item code, generally derived by quantizing collaborative or semantic representations to serve as candidate items identifiers in the context. However, existing methods typically construct separate codes for each modality, leading to higher computational and storage costs and hindering the integration of their complementary strengths. Considering this limitation, we seek to integrate two different modalities into a unified code, fully unleashing the potential of complementary nature among modalities. Nevertheless, the integration remains challenging: the integrated embedding obtained by the common concatenation method would lead to underutilization of collaborative knowledge, thereby resulting in limited effectiveness. To address this, we propose a novel method, named UNGER, which integrates semantic and collaborative knowledge into a unified code for generative recommendation. Specifically, we propose to adaptively learn an integrated embedding through the joint optimization of cross-modality knowledge alignment and next item prediction tasks. Subsequently, to mitigate the information loss caused by the quantization process, we introduce an intra-modality knowledge distillation task, using the integrated embeddings as supervised signals to compensate. Extensive experiments on three widely used benchmarks demonstrate the superiority of our approach compared to existing methods.