Pith Number

pith:5SKZPBJI

pith:2026:5SKZPBJI25FOUMPVS63KKBVCKF

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Dual-Diffusional Generative Fashion Recommendation

Lei Wu, Mingzhe Yu, Qianru Sun, Yunshan Ma

A dual-diffusion Transformer generates both fashion item images and textual descriptions for personalized recommendations.

arxiv:2605.17357 v1 · 2026-05-17 · cs.IR · cs.MM

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Claims

C1strongest claim

DualFashion achieves strong performance in behavior modeling, interpretability, and efficiency compared to state-of-the-art methods on iFashion and Polyvore-U across Personalized Fill-in-the-Blank and Generative Outfit Recommendation tasks.

C2weakest assumption

That conditioning the dual-diffusion Transformer on structured attribute-level captions and visual outfit information from historical interactions sufficiently removes preference-irrelevant information and accurately models user behavior.

C3one line summary

DualFashion introduces a dual-diffusion Transformer with image and text branches that generates both visual items and semantic descriptions for explainable personalized fashion recommendation.

References

52 extracted · 52 resolved · 5 Pith anchors

[1] Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. InICLR. OpenReview.net 2019

[2] Miaomiao Cai, Lei Chen, Yifan Wang, Haoyue Bai, Peijie Sun, Le Wu, Min Zhang, and Meng Wang. 2024. Popularity-Aware Alignment and Contrast for Mitigating Popularity Bias. InKDD. ACM, 187–198 2024

[3] Miaomiao Cai, Zhijie Zhang, Junfeng Fang, Zhiyong Cheng, Xiang Wang, and Meng Wang. 2026. RMBRec: Robust Multi-Behavior Recommendation towards Target Behaviors. InWWW. ACM, 6731–6742 2026

[4] Wen Chen, Pipei Huang, Jiaming Xu, Xin Guo, Cheng Guo, Fei Sun, Chao Li, Andreas Pfadler, Huan Zhao, and Binqiang Zhao. 2019. POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba 2019

[5] Hao Cheng, Shuo Wang, Wensheng Lu, Wei Zhang, Mingyang Zhou, Kezhong Lu, and Hao Liao. 2023. Explainable Recommendation with Personalized Re- view Retrieval and Aspect Learning. InACL (1). Association 2023

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Receipt and verification

First computed	2026-05-20T00:03:54.004774Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

ec95978528d74aea31f597b6a506a251477d8d0d8423b9a481de11403f91a6dd

Aliases

arxiv: 2605.17357 · arxiv_version: 2605.17357v1 · doi: 10.48550/arxiv.2605.17357 · pith_short_12: 5SKZPBJI25FO · pith_short_16: 5SKZPBJI25FOUMPV · pith_short_8: 5SKZPBJI

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/5SKZPBJI25FOUMPVS63KKBVCKF \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: ec95978528d74aea31f597b6a506a251477d8d0d8423b9a481de11403f91a6dd

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
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    "submitted_at": "2026-05-17T09:52:18Z",
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