Asymmetric Generative Recommendation via Multi-Expert Projection and Multi-Faceted Hierarchical Quantization

TL;DR

This paper introduces AsymRec, a novel asymmetric framework for generative recommendation that enhances semantic preservation and target discretization, leading to significant performance improvements over existing methods.

Contribution

It proposes Multi-expert Semantic Projection and Multi-faceted Hierarchical Quantization to decouple input-output representations, addressing key bottlenecks in generative recommendation models.

Findings

AsymRec outperforms state-of-the-art models by 15.8% on average.

MSP improves generalization to infrequent items.

MHQ maintains fine-grained distinctions in structured discrete targets.

Abstract

Generative Recommendation (GenRec) models reformulate recommendation as a sequence generation task, representing items as discrete Semantic IDs used symmetrically as both inputs and prediction targets. We identify a critical dual-stage information bottleneck in this design: (1) the Input Bottleneck, where lossy quantization degrades fine-grained semantics, while popularity bias skews the learned representations toward frequent items, and (2) the Output Bottleneck, where imprecise discrete targets limit supervision quality. To address these issues, we propose AsymRec, an asymmetric continuous-discrete framework that decouples input and output representations. Specifically, Multi-expert Semantic Projection (MSP) maps continuous embeddings into the Transformer's hidden space via expert-specialized projections, preserving semantic richness and improving generalization to infrequent items. Multi-faceted Hierarchical Quantization (MHQ) constructs high-capacity, structured discrete targets through multi-view and multi-level quantization with semantic regularization, preventing dimensional collapse while retaining fine-grained distinctions. Extensive experiments demonstrate that AsymRec consistently outperforms state-of-the-art generative recommenders by an average of 15.8 %. The code will be released.