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SG-UMP: Sequence-Guided Universal Multimodal Prioritization Calculation Framework

Xinyi Zhang, Yutong Li, Peijie Sun

arXiv:2608.28503v1cs.IR

TL;DR

MSR must better capture user-level preference heterogeneity and dataset-level modality bias because existing methods provide limited adaptation across users and datasets. SG-UMP adds sequence-guided multimodal processing through a Module Combiner and dynamically ordered Module Router. Across four real-world datasets, it consistently improves recommendation performance across different backbones and multimodal settings, although its attention visualizations are not reliable measures of module effectiveness.

  • Problem

    Existing MSR methods struggle to capture user-level preference heterogeneity and dataset-level modality bias, limiting adaptation across users and datasets.

  • Method

    SG-UMP is a plug-and-play plugin that combines multimodal processing modules and dynamically routes their execution order using sequence- and dataset-aware information.

  • Results

    SG-UMP consistently improves recommendation performance across four real-world datasets, different backbones, and multimodal settings.

  • Takeaways & Limitations

    Personalized sequence-guided processing and dataset-adaptive module ordering provide a general enhancement for multimodal sequential recommendation.

  • Takeaways & Limitations

    Attention visualizations provide qualitative insights but do not reliably measure the effectiveness of different modules.

Abstract

from arXiv · show

Multimodal sequential recommendation (MSR) improves recommendation by incorporating heterogeneous information such as text, images, and user interactions. However, existing MSR methods often fail to capture user-level preference heterogeneity and dataset-level modality bias, limiting their adaptability across users and datasets. To address this issue, we propose \textbf{S}equence-\textbf{G}uided \textbf{U}niversal \textbf{M}ultimodal \textbf{P}rioritization Calculation Framework (\textbf{SG-UMP}), a plug-and-play plugin for enhancing multimodal information processing in MSR. SG-UMP includes a Module Combiner for flexible multimodal processing and a Module Router for dynamic module ordering, enabling adaptation to both user preferences and dataset characteristics. Experiments on four real-world datasets show that SG-UMP consistently improves recommendation performance across different backbones and multimodal settings. The code is available at https://github.com/esemsc-xz524/SG-UMP .

1 Introduction

Existing MSR methods struggle to personalize multimodal representations across users and adapt processing to dataset-specific modality biases. SG-UMP addresses these issues with sequence-guided multimodal processing and dynamically ordered modules, improving performance across datasets, backbones, and multimodal settings.

  • Motivation: Generic pre-trained encoders often miss user-specific attention patterns, leaving multimodal representations insufficiently personalized.Figure 1 contrasts generic encoder attention with User 1’s focus on sunglasses and User 2’s focus on handbags.
  • Motivation: Different datasets contain distinct modality distributions, dominant information sources, and data qualities, requiring adaptable processing strategies.The framework therefore supports modular and re-orderable processing across dataset characteristics.
  • Results: SG-UMP consistently improves recommendation performance across four real-world datasets, different backbones, and multimodal settings.The results also show that optimal module order varies across datasets, supporting adaptive routing.
  • Framework: User interaction sequences guide personalized multimodal processing, while modular reordering adapts the framework to dataset characteristics.These are presented as two design principles for MSR.
  • Framework: SG-UMP is a plug-and-play plugin with a Module Combiner for flexible multimodal processing and a Module Router for dynamic module ordering.The combiner integrates filter-enhanced, self-attention-based, and Mixture-of-Experts modules with distinct multimodal-processing roles.

2 Preliminary

MSR combines users’ historical interaction sequences with item multimodal features to predict the next item. The preliminary formulation represents items using ID, image, and text information, with embeddings unified for downstream fusion.

  • Problem Definition: MSR models users’ historical interaction sequences together with item multimodal features to predict their next interaction.Historical interactions are organized chronologically for each user.
  • Problem Definition: Each item incorporates an ID, image, and textual description in the multimodal formulation.The derivation focuses on image and text modalities while allowing extension to additional modalities.
  • Representation: Image and text embeddings are obtained with a CLIP encoder, while a trainable ID embedding layer captures collaborative interaction signals.The heterogeneous embeddings are projected into a shared d-dimensional feature space for downstream fusion.

3 Methodology

SG-UMP combines three multimodal processing layers and uses a sequence-guided router to adapt module execution to user preferences and dataset modality composition.

  • Module Combiner: The Module Combiner integrates a Frequency Filter Layer, Hierarchical Attention Layer, and Multi-Scale Fusion Layer for multimodal representation learning.These layers target stable frequency patterns, user-specific modality weighting, and modality-unique or shared feature interactions.
  • Frequency Filter Layer: The Frequency Filter Layer transforms multimodal sequences into the frequency domain, suppresses high-frequency noise, and reconstructs refined time-domain embeddings.It is intended to preserve stable low-frequency signals and produce user-aligned representations.
  • Hierarchical Attention Layer: The Hierarchical Attention Layer uses MLP- and Sigmoid-based modality weighting followed by self-attention to emphasize signals aligned with user preferences.The layer preserves dimensionality for integration with subsequent modules.
  • Multi-Scale Fusion Layer: The Multi-Scale Fusion Layer combines modality-unique and modality-shared experts through a lightweight gating network to preserve modality specificity and cross-modal consistency.The numbers of unique and shared experts are tunable to accommodate differing modality relevance and dataset complexity.
  • Module Router: The Module Router computes modality-aware dispatch scores, converts them into routing distributions, and sorts them into a permutation that determines module execution order.This dataset-adaptive ordering is intended to match input characteristics and dataset-level modality composition.
  • Module Router: Conditional mutual information regularization strengthens modality–module dependencies while encouraging input-dependent and diverse routing behaviors.The objective discourages uniform or degenerate execution orders and encourages alignment between modality cues and module behavior.

4 Experiments

Experiments evaluate SG-UMP across four datasets, multiple backbones, module configurations, attention visualizations, and hyperparameter settings. Results support its effectiveness, adaptive routing, user-focused attention, and practical trade-offs.

  • Principle 1: Removing any core module consistently degrades performance, while the complete SG-UMP achieves the best results across datasets.The ablation identifies complementary contributions from frequency filtering, hierarchical attention, and multi-scale fusion.
  • Principle 2: Optimal module order varies by dataset, with c-b-a for Home, b-a-c for Beauty, a-c-b for Office, and a-b-c for Yelp.These results indicate that no fixed pipeline is universally optimal, and recall-based metrics are more sensitive to ordering than NDCG.
  • Limitations: Attention visualizations provide qualitative insights but do not reliably evaluate module effectiveness, so quantitative results remain primary.Future work will explore more informative visualization and interpretability techniques for module interactions.
  • Overall Comparison: SG-UMP improves SASRec by 21.96%, STOSA by 24.93%, and Oracle4Rec by 18.91% on average across four datasets.The framework consistently outperforms the evaluated baselines across different sequential recommendation architectures.
  • Visualization Analysis: SG-UMP produces more focused attention aligned with user-relevant attributes than STOSA with pre-trained embeddings.The visualization compares model attention with an approximated user perspective based on salient item features, without manual annotations.
  • Parameter Sensitivity: Performance declines as Frequency Filter Modules increase from 1 to 5, while the best settings are E_m=4, E_s=2, and α=0.5.Additional filters cause over-filtering; excessive task or shared experts reduce performance through redundancy or overfitting, and larger α reduces flexibility.

5 Related Work

Sequential recommendation has progressed from Markov-chain and neural sequence models to Transformer-based approaches, while multimodal recommendation incorporates images and text to capture preferences. Recent work extends these ideas to multimodal sequential recommendation, including SG-UMP’s adaptive module composition and processing strategies.

  • Sequential recommendation predicts subsequent items from temporally ordered user interactions, progressing from Markov chains to recurrent and Transformer-based models.
  • Multimodal recommendation uses heterogeneous data such as images and text to better capture user preferences.
  • SG-UMP enables adaptive multimodal modeling through flexible module composition and dynamic processing strategies.

6 Conclusion

SG-UMP enhances multimodal information processing in MSR through flexible module combination and dynamic ordering. Across multiple real-world datasets, it consistently improves recommendation performance across backbones and multimodal settings.

  • SG-UMP addresses user-level preference heterogeneity and dataset-level modality bias with a Module Combiner and Module Router.
  • Experiments show consistent recommendation improvements across different backbones and multimodal settings.
  • Future work will explore more efficient routing strategies and additional modalities and recommendation scenarios.

A.1 Low-Pass Filtering Model

The Low-Pass Filtering Model transforms multimodal sequences into the frequency domain, removes high-frequency noise, reconstructs stable signals, and combines them with the original input.

  • The model refines input sequences by applying Fourier transforms to extract stable low-frequency components and suppress high-frequency noise.
  • FFT transforms the input sequence into frequency components, while a dynamic cutoff retains low-frequency patterns in the spectrum.
  • The refined spectrum matrix is formed by element-wise multiplying the spectrum with a mask that retains low-frequency components.
  • IFFT reconstructs the filtered spectrum in the time domain while preserving stable features.
  • A residual connection and LayerNorm combine the reconstructed signal with the original input to preserve valuable high-frequency information.
  • The model isolates patterns, suppresses noise, and aligns refined sequences with user attention for integration with recommendation modules.

A.2.1 Preliminary on Partial Information Decomposition Theory.

Partial Information Decomposition separates information from two sources about a target into unique, redundant, and synergistic components. SG-UMP connects this decomposition to router design through conditional mutual information measuring modality–module dependencies.

  • PID decomposes information that two sources provide about a target into four distinct components.
  • Unique information comes from only one source, redundant information overlaps across sources, and synergy appears only when both are jointly observed.
  • Synergy measures information gained from joint modality interaction beyond what modalities provide independently.
  • Conditional mutual information serves as an operational synergy measure for a module and modality conditioned on the input embedding.
  • Maximizing I(M;m|e_i) encourages routing that exploits modality–module dependencies and synergistic interactions.
  • High I(M;m|e_i) indicates modality-specific routing cues, whereas low values produce modality-agnostic and degenerate assignments.

A.3 Algorithm

SG-UMP combines multimodal processing modules and dynamically routes their execution order, while training jointly optimizes recommendation and mutual-information objectives. Its complexity includes frequency filtering, attention, multi-scale fusion, and routing components, and evaluation compares traditional and multimodal recommendation baselines.

  • Time Complexity: The framework’s theoretical complexity sums frequency filtering, hierarchical attention, multi-scale fusion, and module routing costs.The total is O(N·L·dlog L + M·L^2·d + P·L·K·d + d^2).
  • Training Procedure: SG-UMP training uses multimodal embeddings, three modules, router parameters, and a hyperparameter controlling mutual-information optimization.The procedure encodes modality-specific representations, determines a routing order, computes mutual information loss, and jointly optimizes L = Lrec + αLMI.
  • Time Complexity: Attention dominates in practice when L≫d, whereas router cost remains minor even when d> L.The attention contribution is M·L^2·d, while routing requires O(d^2) under small module and modality counts.
  • Baselines: Baseline comparisons cover traditional sequential or graph-based recommenders and multimodal recommendation systems.The listed traditional baselines include SASRec, BERT4-Rec, LightGCN, STOSA, FMLP-Rec, and Oracle4Rec; multimodal baselines include VBPR, MMMLP, MML, MMSR, MMSBR, and MP4SR.
  • Baselines: Table 4 reports average training time per epoch for baseline models with and without SG-UMP.Values are in seconds, averaged over five runs, with bold formatting for SG-UMP variants and reported statistical significance at p< 0.05.
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