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Paper Citation Record · LEDGER

Tokenizing Numerical and Embedding Features for LLM RecSys

As of 21 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2607.10016.

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

pith.paper-citation-record.v1
2607.10016 v3

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:43:27.287683Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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  • unresolved48
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External citation measurements

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Outbound references

Observation 4c092fa5-1e76-4003-9725-295d5399b46a · outbound

This paper cites Proceedings of the 16th ACM Conference on Recommender Systems , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 16th ACM Conference on Recommender Systems , pages =

Reference 1

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Observation 15f3d302-5845-4125-8518-2197ff90e059 · outbound

This paper cites Proceedings of the 17th ACM Conference on Recommender Systems , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 17th ACM Conference on Recommender Systems , pages =

Reference 2

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Observation 1bf945cd-922f-4bc8-8b33-9b32b211c9f9 · outbound

This paper cites Proceedings of the 1st Workshop on Deep Learning for Recommender Systems , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 1st Workshop on Deep Learning for Recommender Systems , pages =

Reference 3

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Observation 0f7182ec-7b35-4380-8434-8d9d95e0cad3 · outbound

This paper cites Proceedings of the 26th International Joint Conference on Artificial Intelligence , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 26th International Joint Conference on Artificial Intelligence , pages =

Reference 4

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Observation 6f0c9429-84f5-4ce9-a803-b55016ad49c1 · outbound

This paper cites Proceedings of the ADKDD'17 , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the ADKDD'17 , pages =

Reference 5

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Observation dd45892f-07b7-484c-8012-7341df41bc08 · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

Tokenizing Numerical and Embedding Features for LLM RecSys Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 6

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Observation 470f51c2-c2a3-4bf5-857e-c6dc517a5908 · outbound

This paper cites Proceedings of the 10th ACM Conference on Recommender Systems , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 10th ACM Conference on Recommender Systems , pages =

Reference 7

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Observation 271d35e8-b92e-4d0c-bdc0-ef54fa4ea983 · outbound

This paper cites Proceedings of the 13th ACM Conference on Recommender Systems , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 13th ACM Conference on Recommender Systems , pages =

Reference 8

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Observation ccc05f3a-f0d6-41c5-894b-dc69601af3a2 · outbound

This paper cites Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing , pages =

Reference 9

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Observation a9a44279-b2ba-460b-be25-26570da200f6 · outbound

This paper cites Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , pages =

Reference 10

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Observation 00568f28-ca8c-4004-98ae-797de2bc7560 · outbound

This paper cites Proceedings of the 40th International Conference on Machine Learning , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 40th International Conference on Machine Learning , pages =

Reference 11

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Observation d357c7b8-ec05-4c20-b30d-a9a3b6071d06 · outbound

This paper cites A Survey on Large Language Models for Recommendation.

Tokenizing Numerical and Embedding Features for LLM RecSys A Survey on Large Language Models for Recommendation

Reference 12

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Observation 118793f2-99da-4f19-b1e9-abb6f1862224 · outbound

This paper cites Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis.

Tokenizing Numerical and Embedding Features for LLM RecSys Tapping the Potential of Large Language Models as Recommender Systems: A Comprehensive Framework and Empirical Analysis

Reference 13

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Observation 49bc954f-a104-4373-a9b2-41679d887b46 · outbound

This paper cites Proceedings of the ACM Web Conference 2024 , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the ACM Web Conference 2024 , pages =

Reference 14

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Observation ded0526b-e31c-4545-9c85-0097769fbaa7 · outbound

This paper cites Proceedings of the 17th ACM International Conference on Web Search and Data Mining , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 17th ACM International Conference on Web Search and Data Mining , pages =

Reference 15

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Observation c381f2f0-1920-45a5-965d-f329c5e1aca2 · outbound

This paper cites LLM-Rec: Personalized Recommendation via Prompting Large Language Models.

Tokenizing Numerical and Embedding Features for LLM RecSys LLM-Rec: Personalized Recommendation via Prompting Large Language Models

Reference 16

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Observation 8dc0a6c3-e802-4bf4-83b4-6036b08a5114 · outbound

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Tokenizing Numerical and Embedding Features for LLM RecSys Large Language Models for Generative Recommendation: A Survey and Visionary Discussions

Reference 17

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Observation eafe4ada-a110-480f-baed-3a13372b4930 · outbound

This paper cites Advances in Neural Information Processing Systems , volume =.

Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , volume =

Reference 18

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Observation 59678f08-2663-4b38-925c-51f9c316117f · outbound

This paper cites Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =

Reference 19

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Observation 1af4dcae-55a3-48d3-a1f2-66ee0f200e2f · outbound

This paper cites Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

Reference 20

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Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

Reference 21

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This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the AAAI Conference on Artificial Intelligence , volume =

Reference 22

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Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 13th ACM Conference on Recommender Systems , pages =

Reference 23

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This paper cites Proceedings of the 28th ACM International Conference on Information and Knowledge Management , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 28th ACM International Conference on Information and Knowledge Management , pages =

Reference 24

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Observation abea83ea-2411-4375-830d-46ec25a2dde7 · outbound

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Tokenizing Numerical and Embedding Features for LLM RecSys TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 25

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Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , year =

Reference 26

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Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , year =

Reference 27

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Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , volume =

Reference 28

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Tokenizing Numerical and Embedding Features for LLM RecSys European Conference on Computer Vision , pages =

Reference 29

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Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , year =

Reference 30

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Tokenizing Numerical and Embedding Features for LLM RecSys Advances in Neural Information Processing Systems , year =

Reference 31

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Tokenizing Numerical and Embedding Features for LLM RecSys IEEE Transactions on Pattern Analysis and Machine Intelligence , year =

Reference 32

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Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 25th International Conference on World Wide Web , series =

Reference 33

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Tokenizing Numerical and Embedding Features for LLM RecSys Qwen3 Technical Report

Reference 34

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Tokenizing Numerical and Embedding Features for LLM RecSys International Conference on Learning Representations , year =

Reference 35

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Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining , pages =

Reference 36

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Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , pages =

Reference 37

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Tokenizing Numerical and Embedding Features for LLM RecSys 2018 IEEE International Conference on Data Mining , pages =

Reference 38

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source=arxiv_source observed=2026-08-04T01:43:26.017630Z digest=sha256:d076793696bd71a457295a7e9388547713d75edba7bd83f3866949546d0f7e3a

Observation f3ea8a95-ae87-4581-a523-358a3f6443b2 · outbound

This paper cites 2019 , publisher =.

Tokenizing Numerical and Embedding Features for LLM RecSys 2019 , publisher =

Reference 39

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no resolver link, observed 2026-08-04T01:43:26.179619Z

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source=arxiv_source observed=2026-08-04T01:43:26.179619Z digest=sha256:bebca99edd49443229e1858332264ffb5a598f4297da2adb4156e9850c6f7a3a

Observation 4e8e8df1-56e6-4a4b-9968-ebff2d1c4306 · outbound

This paper cites Proceedings of the 28th International Joint Conference on Artificial Intelligence , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 28th International Joint Conference on Artificial Intelligence , pages =

Reference 40

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no resolver link, observed 2026-08-04T01:43:26.248813Z

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source=arxiv_source observed=2026-08-04T01:43:26.248813Z digest=sha256:4db987ca19988b6629d99a1f6e21f6c58a95379bb7cddd67cec319f167daf535

Observation 76459fb8-300b-49f1-a842-bc6210ef5eba · outbound

This paper cites 2020 , publisher =.

Tokenizing Numerical and Embedding Features for LLM RecSys 2020 , publisher =

Reference 41

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no resolver link, observed 2026-08-04T01:43:26.354514Z

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source=arxiv_source observed=2026-08-04T01:43:26.354514Z digest=sha256:306ef0dfe6a6d537e6a830e882ed693e3e9b115e87dc14f1852cd01ce17bbb63

Observation 454b4856-ae02-453d-81f2-0c383713736a · outbound

This paper cites 2022 IEEE 38th International Conference on Data Engineering , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys 2022 IEEE 38th International Conference on Data Engineering , pages =

Reference 42

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no resolver link, observed 2026-08-04T01:43:26.455608Z

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source=arxiv_source observed=2026-08-04T01:43:26.455608Z digest=sha256:86a0bb2e55e600beec05ee8b14b2258fc673a13f418347d9260d7efcc7cdd9dc

Observation f966f511-0978-42e8-9d6c-7fbf16c2c99e · outbound

This paper cites Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining , pages =

Reference 43

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no resolver link, observed 2026-08-04T01:43:26.595928Z

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source=arxiv_source observed=2026-08-04T01:43:26.595928Z digest=sha256:dc21ab7209e7aa10d24ae3ece6a2ffe69863caa78c673e948a7ba736df6ddb03

Observation 9f737b88-e01c-4a76-bfed-322f1df7e83b · outbound

This paper cites Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =

Reference 44

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no resolver link, observed 2026-08-04T01:43:26.691724Z

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source=arxiv_source observed=2026-08-04T01:43:26.691724Z digest=sha256:5e1ad463c73fa77d5289858346209c0faf13a9f01e88baf11b465a620acbf2e2

Observation 9af7755d-dfb6-45c4-831a-ff539431fb55 · outbound

This paper cites Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =

Reference 45

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no resolver link, observed 2026-08-04T01:43:26.869901Z

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source=arxiv_source observed=2026-08-04T01:43:26.869901Z digest=sha256:41a49b73c02723bf7ee517343bf70c1670f3995dfac503b8c9dd37c60285e6e4

Observation 17127de2-70d3-4bf2-a4fb-2e4fef462e26 · outbound

This paper cites Proceedings of the 13th International Conference on Web Search and Data Mining , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 13th International Conference on Web Search and Data Mining , pages =

Reference 46

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no resolver link, observed 2026-08-04T01:43:27.009692Z

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source=arxiv_source observed=2026-08-04T01:43:27.009692Z digest=sha256:65e68bd83851867c8177b3df1022313b128b68acfde381408c62d020b0075b25

Observation d6e0e4d0-0313-451a-856b-cbd2874dedc5 · outbound

This paper cites Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , pages =

Reference 47

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no resolver link, observed 2026-08-04T01:43:27.150066Z

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source=arxiv_source observed=2026-08-04T01:43:27.150066Z digest=sha256:499f7aa7ee4a7feffd744128e175256d9dcf600337922810b60a2c451b1559f8

Observation 8780a9ad-761a-432d-b78c-328937b666af · outbound

This paper cites Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages =.

Tokenizing Numerical and Embedding Features for LLM RecSys Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages =

Reference 48

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no resolver link, observed 2026-08-04T01:43:27.287683Z

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source=arxiv_source observed=2026-08-04T01:43:27.287683Z digest=sha256:a50e26dcaced024be4129ad0686514b71bd57041523ce62ffed77bd7315d5f67

Pith citing papers

No inbound Pith citation observations are available.