PRISM: Purified Representation and Integrated Semantic Modeling for Generative Sequential Recommendation

2026-01-23

AI Summary

Generative sequential recommendation faces significant challenges, including impure semantic tokenization, severe information loss, and a lack of hierarchical logical structure. To address these issues, this work proposes the PRISM framework, which enhances token discriminability through a purified semantic quantizer and introduces several key mechanisms: adaptive collaborative denoising, hierarchical semantic anchoring, dynamic semantic integration, and structural alignment. These components collectively mitigate information loss and improve logical consistency in sequence modeling. Extensive experiments on four real-world datasets demonstrate that PRISM substantially outperforms existing state-of-the-art methods, with particularly pronounced gains in highly sparse scenarios.

Technology Category

• Natural Language Processing: Generation
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Generative Sequential Recommendation (GSR) has emerged as a promising paradigm, reframing recommendation as an autoregressive sequence generation task over discrete Semantic IDs (SIDs), typically derived via codebook-based quantization. Despite its great potential in unifying retrieval and ranking, existing GSR frameworks still face two critical limitations: (1) impure and unstable semantic tokenization, where quantization methods struggle with interaction noise and codebook collapse, resulting in SIDs with ambiguous discrimination; and (2) lossy and weakly structured generation, where reliance solely on coarse-grained discrete tokens inevitably introduces information loss and neglects items'hierarchical logic. To address these issues, we propose a novel generative recommendation framework, PRISM, with Purified Representation and Integrated Semantic Modeling. Specifically, to ensure high-quality tokenization, we design a Purified Semantic Quantizer that constructs a robust codebook via adaptive collaborative denoising and hierarchical semantic anchoring mechanisms. To compensate for information loss during quantization, we further propose an Integrated Semantic Recommender, which incorporates a dynamic semantic integration mechanism to integrate fine-grained semantics and enforces logical validity through a semantic structure alignment objective. PRISM consistently outperforms state-of-the-art baselines across four real-world datasets, demonstrating substantial performance gains, particularly in high-sparsity scenarios.

Problem

• Generative Sequential Recommendation
• Semantic Tokenization
• Information Loss
• Hierarchical Logic
• Codebook Collapse

Innovation

• Generative Sequential Recommendation
• Semantic Quantization
• Codebook Denoising
• Hierarchical Semantic Modeling
• Semantic Structure Alignment