MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation

TL;DR

MUFFIN is a novel frequency-domain sequential recommendation model that employs user-adaptive filters across global and local frequency modules, effectively capturing diverse user behaviors and outperforming existing models.

Contribution

Introduces MUFFIN, a frequency-domain SR model with user-specific filters and dual modules, addressing limited frequency coverage and personalization issues in prior models.

Findings

MUFFIN outperforms state-of-the-art models on five datasets.

User-adaptive filters improve personalization in recommendations.

Combining global and local modules enhances pattern capturing.

Abstract

Sequential recommendation (SR) aims to predict users' subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which effectively models periodic patterns in user sequences. However, existing frequency-domain SR models still face two major drawbacks: (i) limited frequency band coverage, often missing critical behavioral patterns in a specific frequency range, and (ii) lack of personalized frequency filtering, as they apply an identical filter for all users regardless of their distinct frequency characteristics. To address these challenges, we propose a novel frequency-domain model, Mixture of User-adaptive Frequency FIlteriNg (MUFFIN), operating through two complementary modules. (i) The global filtering module (GFM) handles the entire frequency spectrum to capture comprehensive behavioral patterns. (ii) The local filtering module (LFM) selectively emphasizes important frequency bands without excluding information from other ranges. (iii) In both modules, the user-adaptive filter (UAF) is adopted to generate user-specific frequency filters tailored to individual unique characteristics. Finally, by aggregating both modules, MUFFIN captures diverse user behavioral patterns across the full frequency spectrum. Extensive experiments show that MUFFIN consistently outperforms state-of-the-art frequency-domain SR models over five benchmark datasets. The source code is available at https://github.com/ilwoong100/MUFFIN.