Paper Citation Record · LEDGER

Learning Item Representations Directly from Multimodal Features for Effective Recommendation

As of 20 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 2 inbound Pith citation observations for arXiv:2505.04960.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.04960 v1

Coverage vector

measured 53 of 53 reference resolution

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measured 55 of 55 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T17:33:30.789820Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T00:42:54.760557Z

Reference resolution

53 of 53 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 93c91814-c9c1-4087-857b-bd56368d6bc0 · outbound

This paper cites Multimodal machine learning: A survey and taxonomy,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multimodal machine learning: A survey and taxonomy,

Reference 1

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Observation 4aef370d-1342-4e88-8b0e-637ad9357248 · outbound

This paper cites Recommender sys- tems leveraging multimedia content,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Recommender sys- tems leveraging multimedia content,

Reference 2

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Observation f87bd943-93f8-405f-a808-9281c165b9ee · outbound

This paper cites Cornac: A comparative framework for multimodal recommender systems,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Cornac: A comparative framework for multimodal recommender systems,

Reference 3

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Observation 3cdb4ad6-5fa6-452d-9db9-80d6665d5c9e · outbound

This paper cites A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions

Reference 4

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Observation f2669a38-9ce6-4b09-be62-341e26386226 · outbound

This paper cites Multimodal recommender systems: A survey,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multimodal recommender systems: A survey,

Reference 5

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Observation ba104de6-cfb1-465c-9d7e-ea8125279e75 · outbound

This paper cites Multimodal pretraining, adaptation, and generation for recommendation: A survey,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multimodal pretraining, adaptation, and generation for recommendation: A survey,

Reference 6

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Observation 4216c2e7-8f5c-4202-8087-bee2623016c2 · outbound

This paper cites Mining latent structures for multimedia recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Mining latent structures for multimedia recommendation,

Reference 7

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Observation 265134ae-4f16-4c99-a53e-d8ee7fde100e · outbound

This paper cites A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation A tale of two graphs: Freezing and denoising graph structures for multimodal recommendation,

Reference 8

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Observation 3337275d-fd81-454a-b4e1-953be9142a27 · outbound

This paper cites Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation

Reference 9

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Observation 63792155-1818-4088-b9d1-063aaf28c146 · outbound

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

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Vbpr: visual bayesian personalized ranking from implicit feedback,

Reference 10

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Observation f4b32952-3191-4f92-84ed-6af21cf9e070 · outbound

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

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multi-view graph convolutional network for multimedia recommendation,

Reference 11

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Observation 00186cad-7d66-4788-86c8-05384321eb8c · outbound

This paper cites Graph-refined convolutional network for multimedia recommendation with implicit feedback,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Graph-refined convolutional network for multimedia recommendation with implicit feedback,

Reference 12

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Observation 1f222f44-5174-4f18-bc42-a4c09231da22 · outbound

This paper cites Attention-guided multi- step fusion: a hierarchical fusion network for multimodal recommenda- tion,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Attention-guided multi- step fusion: a hierarchical fusion network for multimodal recommenda- tion,

Reference 13

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Observation 523dc7d5-47de-447d-a981-e531c5b0fcbd · outbound

This paper cites Lgmrec: Local and global graph learning for multimodal recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Lgmrec: Local and global graph learning for multimodal recommendation,

Reference 14

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Observation d4958c89-8798-40b7-9231-841f996db93b · outbound

This paper cites Bpr: Bayesian personalized ranking from implicit feedback,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Bpr: Bayesian personalized ranking from implicit feedback,

Reference 15

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Observation db1de974-6a8b-4063-881c-800400d1f4f1 · outbound

This paper cites Mmgcn: Multi-modal graph convolution network for personalized recommenda- tion of micro-video,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Mmgcn: Multi-modal graph convolution network for personalized recommenda- tion of micro-video,

Reference 16

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Observation ac9e5b68-741b-4a20-bf87-8279ded99d5a · outbound

This paper cites Towards universal sequence representation learning for recommender systems,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Towards universal sequence representation learning for recommender systems,

Reference 17

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Observation c70135c5-c278-48d2-af3c-f3a0964a7769 · outbound

This paper cites Learning vector- quantized item representation for transferable sequential recommenders,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Learning vector- quantized item representation for transferable sequential recommenders,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.384283Z

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Observation 802b95ea-2932-44b5-8198-8563053f6603 · outbound

This paper cites Are id embeddings nec- essary? whitening pre-trained text embeddings for effective sequential recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Are id embeddings nec- essary? whitening pre-trained text embeddings for effective sequential recommendation,

Reference 19

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Observation 3d070473-bde6-4d85-83de-a5a99b2aa18f · outbound

This paper cites The elephant in the room: rethinking the usage of pre-trained language model in sequential recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation The elephant in the room: rethinking the usage of pre-trained language model in sequential recommendation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.358228Z

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Observation 87ab3267-9e89-4fc4-b37a-1f150e4e9c11 · outbound

This paper cites Dual-view whitening on pre- trained text embeddings for sequential recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Dual-view whitening on pre- trained text embeddings for sequential recommendation,

Reference 21

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Observation 1597e60f-00f3-41aa-9c20-30e173748911 · outbound

This paper cites Where to go next for recommender systems? id-vs. modality-based recommender models revisited,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Where to go next for recommender systems? id-vs. modality-based recommender models revisited,

Reference 22

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Observation f2924912-368f-45c1-8f3e-d8988d1838d1 · outbound

This paper cites Semantic- guided feature distillation for multimodal recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Semantic- guided feature distillation for multimodal recommendation,

Reference 23

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This paper cites Rectifier nonlinearities improve neural network acoustic models,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Rectifier nonlinearities improve neural network acoustic models,

Reference 24

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This paper cites Discrete cosine transform,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Discrete cosine transform,

Reference 25

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This paper cites Faster neural networks straight from jpeg,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Faster neural networks straight from jpeg,

Reference 26

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Discrete cosin trans- former: Image modeling from frequency domain,

Reference 27

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Observation 9129b117-bb34-4a95-ab5a-182f30107ba2 · outbound

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Dctvit: Discrete cosine transform meet vision transformers,

Reference 28

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Dct-former: Ef- ficient self-attention with discrete cosine transform,

Reference 29

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Observation 7f80f9cf-3f39-432e-acb6-ddcf1c446a77 · outbound

This paper cites Latent structure mining with contrastive modality fusion for multimedia recom- mendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Latent structure mining with contrastive modality fusion for multimedia recom- mendation,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.244661Z

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This paper cites Disentangled graph variational auto-encoder for multimodal recommendation with interpretability,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Disentangled graph variational auto-encoder for multimodal recommendation with interpretability,

Reference 31

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Lightgcn: Simplifying and powering graph convolution network for recommenda- tion,

Reference 32

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Observation 3708abfb-ccf8-4ff2-abe8-1f6fc3cf2b0d · outbound

This paper cites Latent structure mining with contrastive modality fusion for multimedia recom- mendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Latent structure mining with contrastive modality fusion for multimedia recom- mendation,

Reference 33

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Observation 10276aaf-a585-42ac-a38a-c6d3e81b02fb · outbound

This paper cites Attention is all you need,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Attention is all you need,

Reference 34

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Observation 575a773b-cc1d-4a48-8a57-88c800101715 · outbound

This paper cites Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering,

Reference 35

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This paper cites A Content-Driven Micro-Video Recommendation Dataset at Scale.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation A Content-Driven Micro-Video Recommendation Dataset at Scale

Reference 36

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Mmrec: Simplifying multimodal recommendation,

Reference 37

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Improving Text Embeddings with Large Language Models

Reference 38

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

Reference 39

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation The Llama 3 Herd of Models

Reference 40

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Self-supervised learning for multimedia recommendation,

Reference 41

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This paper cites Bootstrap latent representations for multi-modal recommen- dation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Bootstrap latent representations for multi-modal recommen- dation,

Reference 42

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This paper cites Understanding the difficulty of training deep feedforward neural networks,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Understanding the difficulty of training deep feedforward neural networks,

Reference 43

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This paper cites Selfcf: A simple framework for self-supervised collaborative filtering,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Selfcf: A simple framework for self-supervised collaborative filtering,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.146655Z

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Observation 655bb0aa-7e62-4f8c-989b-e6a842ffd0c5 · outbound

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Learning Item Representations Directly from Multimodal Features for Effective Recommendation Layer-refined graph convolu- tional networks for recommendation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.134142Z

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Observation 17f52306-5b28-409c-a3f2-04482c8bd8e7 · outbound

This paper cites Multi- modal food recommendation using clustering and self-supervised learn- ing,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multi- modal food recommendation using clustering and self-supervised learn- ing,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.120151Z

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This paper cites Multi-modal food recommendation with health-aware knowledge dis- tillation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multi-modal food recommendation with health-aware knowledge dis- tillation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.107586Z

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Observation e695a58a-0889-4dc8-8f30-3f8048a07366 · outbound

This paper cites Multi-modal discrete collaborative filtering for efficient cold-start recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multi-modal discrete collaborative filtering for efficient cold-start recommendation,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.094898Z

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Observation 45f3e09b-c930-4f92-a8b3-60d0aad26d7a · outbound

This paper cites Multimodal pre-training for sequential recommendation via contrastive learning,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Multimodal pre-training for sequential recommendation via contrastive learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.082312Z

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Observation 8bc74160-0df0-4f96-b7e4-4223d51b9941 · outbound

This paper cites Deepstyle: Learning user preferences for visual recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Deepstyle: Learning user preferences for visual recommendation,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.069909Z

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Observation f76fc906-bea8-4cac-912d-b7868ccf89d7 · outbound

This paper cites Atten- tive collaborative filtering: Multimedia recommendation with item-and component-level attention,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Atten- tive collaborative filtering: Multimedia recommendation with item-and component-level attention,

Reference 51

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Observation cda897e1-b82f-45aa-a094-e641ba6a1b95 · outbound

This paper cites Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable rec- ommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Personalized fashion recommendation with visual explanations based on multimodal attention network: Towards visually explainable rec- ommendation,

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-15T23:22:53.942784Z

Source-reported events for the cited work

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Observation f1a5e8bc-c7d5-4bbe-b731-cd0eb25ada4b · outbound

This paper cites Mm-frec: Multi-modal enhanced fashion item recommendation,.

Learning Item Representations Directly from Multimodal Features for Effective Recommendation Mm-frec: Multi-modal enhanced fashion item recommendation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:22:54.044371Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation b73b543e-44ac-4702-b9dd-d6fdbfb0e548 · inbound

EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation cites this paper.

EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation Learning Item Representations Directly from Multimodal Features for Effective Recommendation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T17:33:30.789820Z

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Observation 41c1802d-0035-41f6-9d20-e5395dc4e6d5 · inbound

Modality-Aware Identity Construction and Counterfactual Structure Learning for ID-Free Multimodal Recommendation cites this paper.

Modality-Aware Identity Construction and Counterfactual Structure Learning for ID-Free Multimodal Recommendation Learning Item Representations Directly from Multimodal Features for Effective Recommendation

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-20T00:42:54.763990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.