OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation

Miao Sun

cs.IROct 12, 2026

OT-FlowRec: Optimal Transport-Guided Multi-Bridge Flow for Multimodal Sequential Recommendation

Authors: Miao Sun

Organizations: Nanjing University of Science and Technology School of Computer Science and Engineering

Abstract

E-commerce user preferences evolve dynamically within sessions, exhibiting concurrent and multi-centric interest distributions. While existing multimodal sequential recommendation methods leverage global attention to compress historical interactions, they inadequately capture the concurrent nature of multi-interest behaviors and fail to model the underlying multi-centric distribution of user intent. To tackle this issue, we propose OT-FlowRec, an optimal transport-guided multi-bridge flow matching framework that explicitly models multi-interest distributions to facilitate multimodal sequential recommendation. Unlike single-path generative models, OT-FlowRec constructs a mixture of parallel, interest-specific transport flows to model the multi-centric distribution characteristics, where each flow preserves a distinct user interest cluster via an optimal transport plan. Then, a geometric transport mechanism is designed to establish minimum-cost paths from the latent multi-interest distribution to target preference prototypes to provide multi-interest awareness. Moreover, a progressive time-sampling warmup strategy dynamically biases optimization weights toward the final states of the generation process, bridging the gap between continuous global distribution fitting and practical top- $k$ ranking metrics. This approach achieves explicit interest disentanglement, ensuring the preservation of a multi-peaked preference representation instead of collapsing it into a unimodal average. Extensive experiments on three public datasets demonstrate that OT-FlowRec outperforms existing state-of-the-art baselines.

Figures & tables

Figure 1: User interaction paths in the Toys dataset. Colors denote latent interest categories; this shows that multiple interests coexist within a session.

Figure 2: The overall architecture of OT-FlowRec. Top: The end-to-end recommendation pipeline, transforming multimodal item interactions into parallel transport flows. Middle-left: MPSD fuses multimodal features via a softplus gating network and extracts top- $k$ prototypes to construct parallel interest distributions. Bottom-right: OT-MBFM establishes OT couplings via a geometric cost matrix to guide deterministic flow matching along minimum-cost trajectories. Bottom-left: PTW dynamically shifts time-step sampling weights toward terminal generation states ( $t\to 1$ ) via Beta distribution adjustments.

Dataset	Users	Items	Inter.	Sparsity	Avg. Len.
Beauty	22364	12102	198502	99.92%	8.8
Video Games	24304	10679	231780	99.91%	9.5
Toys	19413	11925	167597	99.92%	8.6

Table 1: Statistics of the evaluation datasets.

Baseline	Beauty				Video Games				Toys
	H@10	H@20	N@10	N@20	H@10	H@20	N@10	N@20	H@10	H@20	N@10	N@20
SASRec	0.0401	0.0660	0.0200	0.0265	0.0847	0.1281	0.0423	0.0532	0.0322	0.0497	0.0164	0.0208
GRU4Rec	0.0562	0.0851	0.0298	0.0370	0.0725	0.1122	0.0376	0.0475	0.0454	0.0676	0.0260	0.0316
BERT4Rec	0.0379	0.0571	0.0200	0.0249	0.0708	0.1091	0.0361	0.0456	0.0274	0.0395	0.0154	0.0184
MSSR	0.0583	0.0956	0.0314	0.0448	0.0709	0.1283	0.0415	0.0515	0.0613	0.0950	0.0318	0.0410
DreamRec	0.0228	0.0417	0.0130	0.0176	0.0415	0.0712	0.0221	0.0295	0.0216	0.0391	0.0111	0.0155

Table 2: Overall performance. The best and second-best scores are shown in bold and underlined, respectively.

Figure 3: Vector field comparison between traditional flow matching and OT-FlowRec on the Toys.

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Search space
Learning rate	$\exp(\mathcal{U}[-9.2,-6.0])$
Number of Transformer layers	$\{1,2,3\}$
Hidden dimension $d$	$\{128,256\}$
Hidden dropout / attention dropout	$\mathcal{U}[0.1,0.5]$
Number of prototypes $M$	$\{32,64,96,128\}$
ODE steps	$\{2,4,6,8\}$

Appendix

Table 4: Hyperparameter search space of OT-FlowRec.

Dataset	LR	Layers	$d$	Drop.	Attn Drop.	$M$	ODE	$\tau$	$\epsilon_{\mathrm{OT}}$	Iter.	Warm-up	$\lambda_{\mathrm{Proto}}$	$\lambda_{\mathrm{OT}}$
Beauty	$5.87{\times}10^{-4}$	1	128	0.39755	0.44607	96	6	0.03919	0.12053	10	12	0.11967	0.02177
Toys	$6.322{\times}10^{-4}$	1	256	0.45730	0.26735	96	6	0.02318	0.117279	10	12	0.056853	0.02742
Video_Games	$6.30{\times}10^{-4}$	2	128	0.49551	0.340265	96	6	0.084436	0.081655	5	9	0.12009	0.05440

Appendix

Table 5: Best hyperparameters of OT-FlowRec on each dataset.

Figure 6: Validation N@20 curves of OT-FlowRec and the ablation variants

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Multi-behavior sequential recommendation (MBSR) aims to learn the dynamic and heterogeneous interactions of users' multi-behavior sequences, so as to capture user preferences under target behavior for the next interacted item prediction. Unlike previous methods that adopt unidirectional modeling by mapping auxiliary behaviors to target behavior, recent concerns are shifting from behavior-fixed to behavior-specific recommendation. However, these methods still ignore the user's latent preference that underlying decision-making, leading to suboptimal solutions. Meanwhile, due to the asymmetric deterministic between items and behaviors, discriminative paradigm based on preference scoring is unsuitable to capture the uncertainty from low-entropy behaviors to high-entropy items, failing to provide efficient and diverse recommendation. To address these challenges, we propose FatsMB, a framework based diffusion model that guides preference generation From Behavior-Agnostic To Behavior-Specific in latent spaces, enabling diverse and accurate Multi-Behavior Sequential Recommendation. Specifically, we design a Multi-Behavior AutoEncoder (MBAE) to construct a unified user latent preference space, facilitating interaction and collaboration across Behaviors, within Behavior-aware RoPE (BaRoPE) employed for multiple information fusion. Subsequently, we conduct target behavior-specific preference transfer in the latent space, enriching with informative priors. A Multi-Condition Guided Layer Normalization (MCGLN) is introduced for the denoising. Extensive experiments on real-world datasets demonstrate the effectiveness of our model.

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