AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS | Paper Citation Record

Observation 25ab7059-decf-4c0d-b263-83ab1d2b020b · outbound

This paper cites Deep neural networks for youtube recommendations.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Deep neural networks for youtube recommendations

Reference 1

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Observation f4bcaf12-f721-4a86-9c8a-3ec68b77f326 · outbound

This paper cites Parameter-efficient transfer from sequential behaviors for user modeling and recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Parameter-efficient transfer from sequential behaviors for user modeling and recommendation

Reference 2

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Observation 39255b05-381e-4829-87a1-d996a8310fbd · outbound

This paper cites Matrix factorization techniques for recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Matrix factorization techniques for recommender systems

Reference 3

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Observation de8e6342-9544-46a6-b027-86a22f758226 · outbound

This paper cites A review of modern fashion recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS A review of modern fashion recommender systems

Reference 4

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Observation 6ee81ede-e003-43ff-9864-a8bfad5d7d98 · outbound

This paper cites Vbpr: visual bayesian personalized ranking from implicit feedback.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Vbpr: visual bayesian personalized ranking from implicit feedback

Reference 5

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Observation b47edd31-aa8b-4469-8a4d-bae4408bd57b · outbound

This paper cites Aesthetic-based clothing recommen- dation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Aesthetic-based clothing recommen- dation

Reference 6

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Observation 1ef73e13-96df-4264-a6f0-b9a86925df63 · outbound

This paper cites Attacking visually-aware recommender systems with transferable and imperceptible adversarial styles.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Attacking visually-aware recommender systems with transferable and imperceptible adversarial styles

Reference 7

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Observation 9e161301-2593-4daf-97f0-7623fca03268 · outbound

This paper cites Adversarial item promotion: Vulnerabilities at the core of top-n recommenders that use images to address cold start.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Adversarial item promotion: Vulnerabilities at the core of top-n recommenders that use images to address cold start

Reference 8

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Observation c93816c3-de0a-477a-ac35-89a72ff7b55a · outbound

This paper cites Adversarial item promotion on visually-aware recommender systems by guided diffusion.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Adversarial item promotion on visually-aware recommender systems by guided diffusion

Reference 9

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Observation 33b051a3-13e5-419c-87b7-be2a59cfd112 · outbound

This paper cites Attacking click-through rate predictors via generating realistic fake samples.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Attacking click-through rate predictors via generating realistic fake samples

Reference 10

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Observation 7c486ce9-205a-419a-80df-69ab4fd51863 · outbound

This paper cites ToDA: Target-oriented Diffusion Attacker against Recommendation System.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS ToDA: Target-oriented Diffusion Attacker against Recommendation System

Reference 11

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Observation d40f76ad-649d-48f2-b033-a9b81f53c0d7 · outbound

This paper cites Adver- sarial attacks for black-box recommender systems via copying transferable cross-domain user profiles.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Adver- sarial attacks for black-box recommender systems via copying transferable cross-domain user profiles

Reference 12

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Observation 466701d3-28de-4032-b93a-f9ce45cbf84e · outbound

This paper cites Influence-driven data poisoning for robust recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Influence-driven data poisoning for robust recommender systems

Reference 13

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Observation d1f592b3-1ebf-4d6f-9816-3ed59c18c2bf · outbound

This paper cites Poisoning gnn-based recommender systems with generative surrogate-based attacks.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Poisoning gnn-based recommender systems with generative surrogate-based attacks

Reference 14

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Observation 67bbb62f-374d-483c-9514-d5f537806279 · outbound

This paper cites Poisoning attacks to graph-based recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Poisoning attacks to graph-based recommender systems

Reference 15

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Observation f768b4c0-edf8-478f-a6af-020e9aa87c8c · outbound

This paper cites Shilling attacks against recommender systems: a comprehensive survey.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Shilling attacks against recommender systems: a comprehensive survey

Reference 16

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Observation 76f044b5-7a61-4638-ad68-824f3e7df69e · outbound

This paper cites Data Poisoning Attacks to Deep Learning Based Recommender Systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Data Poisoning Attacks to Deep Learning Based Recommender Systems

Reference 17

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Observation 1217ca96-8b31-42f3-a68f-e653da634ede · outbound

This paper cites Triple adversarial learning for influence based poisoning attack in recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Triple adversarial learning for influence based poisoning attack in recommender systems

Reference 18

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Observation f9a559ec-783d-4ac7-9b79-0f40f3c6093a · outbound

This paper cites Fight fire with fire: Towards robust recommender systems via adversarial poisoning training.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Fight fire with fire: Towards robust recommender systems via adversarial poisoning training

Reference 19

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Observation 63d23e66-d25b-41f3-bbbf-557b7d9825da · outbound

This paper cites Learning transferable visual models from natural language supervision.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Learning transferable visual models from natural language supervision

Reference 20

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Observation 74882b88-1ba8-4e65-b218-1906d4061990 · outbound

This paper cites Towards Modality Generalization: A Benchmark and Prospective Analysis.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Towards Modality Generalization: A Benchmark and Prospective Analysis

Reference 21

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Observation dab34ed6-8805-4da7-8d5e-6ca1479f95fc · outbound

This paper cites Continual multimodal contrastive learning.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Continual multimodal contrastive learning

Reference 22

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Observation 19053ec4-3610-4889-9ba7-492deba50ec7 · outbound

This paper cites Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos

Reference 23

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Observation bcdd39b5-4e7e-4860-a306-3420965b18e4 · outbound

This paper cites Large scale visual recommendations from street fashion images.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Large scale visual recommendations from street fashion images

Reference 24

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Observation b68640bb-4e10-4218-b7cc-27e607d49494 · outbound

This paper cites Deep residual learning for image recognition.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Deep residual learning for image recognition

Reference 25

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Observation 16a95242-dcf5-4c99-8546-23a65bfe2cc8 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 26

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Observation 857864cc-fbda-4a8b-b44d-8197b3ac0cb1 · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 27

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Observation 9eb1be45-2841-42f5-b35d-0b6a4da837b8 · outbound

This paper cites Neural collaborative filtering.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Neural collaborative filtering

Reference 28

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Observation fe38bba2-ba49-4617-8d66-04b20d5c820b · outbound

This paper cites Image-based recommendations on styles and substitutes.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Image-based recommendations on styles and substitutes

Reference 29

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This paper cites Elimrec: Eliminating single-modal bias in multimedia recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Elimrec: Eliminating single-modal bias in multimedia recommendation

Reference 30

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Observation 576adc62-6052-4d66-a079-04d3886e85e6 · outbound

This paper cites Self- supervised learning for multimedia recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Self- supervised learning for multimedia recommendation

Reference 31

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Observation f49754b8-0d48-4c85-a6da-ae016bb3de84 · outbound

This paper cites Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping

Reference 32

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Observation 2ed45db6-70aa-47ec-9137-c68fd2544b22 · outbound

This paper cites Visually-aware fashion recommendation and design with generative image models.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Visually-aware fashion recommendation and design with generative image models

Reference 33

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Observation ebbdd369-ecba-4d83-b337-88f529bd0ffa · outbound

This paper cites Adversarial training towards robust multimedia recommender system.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Adversarial training towards robust multimedia recommender system

Reference 34

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Observation 14e98fe9-95ba-4573-ae65-df83cca7d0d2 · outbound

This paper cites Shilling black-box recommender systems by learning to generate fake user profiles.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Shilling black-box recommender systems by learning to generate fake user profiles

Reference 35

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Observation c18d8b4f-b69f-4018-a0b5-5526f3763627 · outbound

This paper cites Diffcl: A diffusion-based contrastive learning framework with semantic alignment for multimodal recommendations.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Diffcl: A diffusion-based contrastive learning framework with semantic alignment for multimodal recommendations

Reference 36

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This paper cites Robust privacy-preserving recommendation systems driven by multimodal federated learning.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Robust privacy-preserving recommendation systems driven by multimodal federated learning

Reference 37

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Observation a5b290cb-200e-44b0-97b8-bbbbe3599f44 · outbound

This paper cites A survey on federated recommendation systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS A survey on federated recommendation systems

Reference 38

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This paper cites Multi-view graph convolutional network for multimedia recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Multi-view graph convolutional network for multimedia recommendation

Reference 39

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Observation 15918c77-e0ea-4382-9e03-a949063522a3 · outbound

This paper cites Llmrec: Large language models with graph augmentation for recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Llmrec: Large language models with graph augmentation for recommendation

Reference 40

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Observation 70efd63c-b4ce-4d52-b8fd-bfdc5a6db03a · outbound

This paper cites MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation

Reference 41

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Observation 124a3fc3-6f2b-44f1-a085-a7d04be29517 · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommendation.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:52.377936Z

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Observation 81387a84-740b-447c-b015-0e830b638f56 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS High-resolution image synthesis with latent diffusion models

Reference 43

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Observation dace5166-cf05-495e-8efd-bed7511c5f6a · outbound

This paper cites Taamr: Targeted adversarial attack against multimedia recommender systems.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Taamr: Targeted adversarial attack against multimedia recommender systems

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:52.217977Z

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Observation 2819c5f9-971d-4ced-8a31-3d78bcb0cdfa · outbound

This paper cites The youtube video recommendation system.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS The youtube video recommendation system

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:52.007990Z

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Observation 5f066f7f-d78b-483d-84ec-f67d0bb70664 · outbound

This paper cites Billion-scale commodity embedding for e-commerce recommendation in alibaba.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Billion-scale commodity embedding for e-commerce recommendation in alibaba

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:51.820030Z

Source-reported events for the cited work

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Observation 6d71f39b-c384-4114-bf6d-6b6b00d1d66d · outbound

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AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Unresolved cited work

Reference 47

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Observation 87645ab7-6d69-4bbd-94b2-68c71e6ac8c3 · outbound

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AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Unresolved cited work

Reference 48

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Observation 2d38e8f6-b664-4d2b-9559-973e47ff7615 · outbound

This paper cites Detecting adversarial examples via reconstruction-based semantic inconsistency.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Detecting adversarial examples via reconstruction-based semantic inconsistency

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:51.320852Z

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Observation 9e9cf3f0-1c3f-4c32-9e66-c4188076a24d · outbound

This paper cites Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:51.110681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fbf96a95-2d54-4b07-9e11-b4da548ed898 · outbound

This paper cites Adversarial examples are not bugs, they are features.

AUV-Fusion: Cross-Modal Adversarial Fusion of User Interactions and Visual Perturbations Against VARS Adversarial examples are not bugs, they are features

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:16:50.937533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Outbound references

Pith citing papers