M2Rec: Multi-scale Mamba for Efficient Sequential Recommendation

TL;DR

M2Rec introduces a multi-scale recommendation framework that combines frequency analysis, semantic embeddings, and adaptive fusion to improve efficiency and accuracy in sequential recommendation systems.

Contribution

The paper proposes M2Rec, integrating FFT, LLM embeddings, and gating mechanisms into Mamba for enhanced multi-scale pattern recognition and multimodal feature fusion.

Findings

Achieves 3.2% higher Hit Rate@10 than existing models.

Maintains 20% faster inference compared to Transformer baselines.

Demonstrates state-of-the-art performance in sequential recommendation.

Abstract

Sequential recommendation systems aim to predict users' next preferences based on their interaction histories, but existing approaches face critical limitations in efficiency and multi-scale pattern recognition. While Transformer-based methods struggle with quadratic computational complexity, recent Mamba-based models improve efficiency but fail to capture periodic user behaviors, leverage rich semantic information, or effectively fuse multimodal features. To address these challenges, we propose \model, a novel sequential recommendation framework that integrates multi-scale Mamba with Fourier analysis, Large Language Models (LLMs), and adaptive gating. First, we enhance Mamba with Fast Fourier Transform (FFT) to explicitly model periodic patterns in the frequency domain, separating meaningful trends from noise. Second, we incorporate LLM-based text embeddings to enrich sparse interaction data with semantic context from item descriptions. Finally, we introduce a learnable gate mechanism to dynamically balance temporal (Mamba), frequency (FFT), and semantic (LLM) features, ensuring harmonious multimodal fusion. Extensive experiments demonstrate that \model\ achieves state-of-the-art performance, improving Hit Rate@10 by 3.2\% over existing Mamba-based models while maintaining 20\% faster inference than Transformer baselines. Our results highlight the effectiveness of combining frequency analysis, semantic understanding, and adaptive fusion for sequential recommendation. Code and datasets are available at: https://anonymous.4open.science/r/M2Rec.