A dual-level denoising framework that combines graph Laplacian smoothing and learnable FFT filtering improves multi-modal sequential recommendation on four benchmarks.
A Survey on Sequential Recommendation
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abstract
Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the interacted items, which has received significant attention from both researchers and practitioners. In recent years, we have witnessed great progress and achievements in this field, necessitating a new survey. In this survey, we study the SR problem from a new perspective (i.e., the construction of an item's properties), and summarize the most recent techniques used in sequential recommendation such as pure ID-based SR, SR with side information, multi-modal SR, generative SR, LLM-powered SR, ultra-long SR and data-augmented SR. Moreover, we introduce some frontier research topics in sequential recommendation, e.g., open-domain SR, data-centric SR, could-edge collaborative SR, continuous SR, SR for good, and explainable SR. We believe that our survey could be served as a valuable roadmap for readers in this field.
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2026 1verdicts
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Beyond Noisy Signals: Dual-Level Denoising for Multi-modal Sequential Recommendation
A dual-level denoising framework that combines graph Laplacian smoothing and learnable FFT filtering improves multi-modal sequential recommendation on four benchmarks.