Efficient Multimodal Generative Recommendation with Latent Narrative Reasoning

2026-09-13

AI Summary

本文提出NarraLite框架，通过压缩感知和推理解决多模态生成推荐中的效率问题，提高故事连贯性和准确性。

Abstract

Generative recommendation reformulates item prediction as semantic identifier generation, yet episodic content introduces a fundamentally different setting where the target is determined by narrative evolution rather than user preference. This task requires models to understand multimodal storyline progression while addressing the efficiency challenges caused by redundant visual contexts and costly explicit reasoning generation. We propose \textbf{NarraLite}, an efficient multimodal generative recommendation framework that jointly compresses perception and reasoning. Specifically, Progressive Spectral Compression selectively distills long visual contexts into compact narrative-relevant evidence, preserving transition-critical information while reducing redundant visual computation. Latent Narrative Reasoning introduces context-routed latent reasoning tokens and aligns their contextualized representations with future continuation semantics, enabling implicit narrative inference without autoregressively decoding textual rationales. We further establish a user-agnostic multimodal benchmark for short-form drama continuation across UGC, PGC, and OOD settings. Extensive experiments demonstrate that NarraLite consistently improves continuation accuracy, narrative coherence, and robustness over existing approaches, while achieving a favorable accuracy--efficiency trade-off.

Problem

• Generative Recommendation
• Multimodal Storyline Progression
• Efficiency Challenges
• Redundant Visual Contexts
• Explicit Reasoning Generation

Innovation

• Progressive Spectral Compression
• Latent Narrative Reasoning
• multimodal generative recommendation