Harmonizing Generative Retrieval and Ranking in Chain-of-Recommendation

2026-04-28

AI Summary

This work addresses the performance bottleneck in generative recommender systems, which often underperform due to limited ranking capabilities after candidate generation. To bridge this gap, we propose RecoChain, a novel framework that unifies generative retrieval and ranking within a single Transformer backbone for the first time. RecoChain first efficiently generates candidate items through hierarchical semantic ID prediction and then refines their ranking via a Semantic Interaction Modeling (SIM) mechanism that captures fine-grained click likelihood. This end-to-end co-optimization effectively aligns the generative and ranking objectives, closing the performance gap between them. Extensive experiments on large-scale real-world datasets demonstrate that RecoChain significantly improves Top-K recommendation accuracy while preserving strong generative capacity.

Abstract

Generative recommender systems have recently emerged as a promising paradigm by formulating next-item prediction as an auto-regressive semantic IDs generation, such as OneRec series works. However, with the next-item-agnostic prediction paradigm, its could beam out some next potential items via Semantic IDs but hard to estimate which items are better from them, e.g., select the top-10 from beam-256 items, leading to a gap between generation and ranking performance. To fulfill this gap, we propose RecoChain, a unified generative retrieval and ranking framework that integrates candidate generation and ranking within a single Transformer backbone. Specifically, in inference, the model first generates candidate items via hierarchical semantic ID prediction, then performs the SIM-based ranking process to estimate the click possibility of corresponding item candidate continuously. Extensive experiments on large-scale real-world datasets demonstrate that our approach effectively bridges the gap between generative retrieval and ranking, achieving improved Top-K recommendation performance while maintaining strong generative capability.

Problem

• Generative Recommender Systems
• Next-item Prediction
• Candidate Ranking
• Semantic IDs
• Retrieval-Ranking Gap

Innovation

• Generative Retrieval
• Ranking Integration
• Semantic ID
• Transformer-based Recommendation
• SIM-based Ranking