Paper Citation Record · LEDGER

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation

As of 10 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2504.13614.

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

pith.paper-citation-record.v1
2504.13614 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T19:24:48.300461Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact6
verified fuzzy32
unresolved5
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d29b7163-eb24-44aa-99a0-f3e072683d07 · outbound

This paper cites Transformers need glasses! Information over-squashing in language tasks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Transformers need glasses! Information over-squashing in language tasks

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:25:03.714531Z

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Observation ca56b91e-3bc1-4e50-a2d6-3a71cbb34ace · outbound

This paper cites Best of Both Worlds: Advantages of Hybrid Graph Sequence Models.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Best of Both Worlds: Advantages of Hybrid Graph Sequence Models

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:25:03.730397Z

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Observation 037b341f-8641-47ae-be83-dc969bad8328 · outbound

This paper cites Fast unfolding of communities in large networks: 15 years later.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Fast unfolding of communities in large networks: 15 years later

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:25:03.723962Z

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Observation 98c94683-377a-4903-9c5b-93a99a55268b · outbound

This paper cites Half a decade of graph convolutional networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Half a decade of graph convolutional networks

Reference 4

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Observation 3e5c7245-0277-49ea-b26a-817e0f5dc70e · outbound

This paper cites Revisiting graph based collab- orative filtering: A linear residual graph convolutional network approach.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Revisiting graph based collab- orative filtering: A linear residual graph convolutional network approach

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.184996Z

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Observation 8bee33ef-61fe-48fd-b113-04ba0aec1569 · outbound

This paper cites Myers, and Jure Leskovec.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Myers, and Jure Leskovec

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.203296Z

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Observation 9e04caa4-63a8-4f7a-9865-8250146e59de · outbound

This paper cites Unified denoising training for recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unified denoising training for recommendation

Reference 7

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Observation 3c471288-f8ee-4f72-a390-b5afad5e3711 · outbound

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Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unresolved cited work

Reference 8

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Observation 5aeaca14-adfc-4916-b842-11e007e74337 · outbound

This paper cites A survey of graph neural networks for recom- mender systems: Challenges, methods, and directions.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation A survey of graph neural networks for recom- mender systems: Challenges, methods, and directions

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.172680Z

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Observation fbc56692-10fc-4f61-a243-9dcca8de5d03 · outbound

This paper cites Content augmented graph neural networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Content augmented graph neural networks

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.196922Z

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Observation 64d7e8b0-7646-4ed6-bc8d-6bb296e0e6fd · outbound

This paper cites Heterophily-Aware Fair Recommendation using Graph Convolutional Networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Heterophily-Aware Fair Recommendation using Graph Convolutional Networks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:25:03.720322Z

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Observation 8743cf51-9c1d-4f1b-afe8-324bea068da5 · outbound

This paper cites Disentangling popularity and quality: An edge classification approach for fair recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Disentangling popularity and quality: An edge classification approach for fair recommendation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.194704Z

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Observation 2be43bb8-94c4-461e-be11-315a8aeb86b3 · outbound

This paper cites Maxwell Harper and Joseph A.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Maxwell Harper and Joseph A

Reference 13

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Observation dc769f10-836b-40ea-9006-b02ec8838cdb · outbound

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Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unresolved cited work

Reference 14

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Observation 59c89268-d660-4f81-94da-75814129e129 · outbound

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

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 15

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Observation 8b2b3d2b-59cd-4483-b2ce-e2d3e1e35520 · outbound

This paper cites Neural collaborative filtering.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Neural collaborative filtering

Reference 16

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Observation 193c7cc6-b8e2-49de-8395-9c0af21d6105 · outbound

This paper cites Session-based recommendations with recurrent neural networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Session-based recommendations with recurrent neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.185270Z

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Observation 5a47c67b-5759-42fc-a133-b50e81e9c0de · outbound

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Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unresolved cited work

Reference 18

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Observation f65ed63f-c24a-43ce-a11f-8fd14629c82d · outbound

This paper cites Kipf and Max Welling.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Kipf and Max Welling

Reference 19

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Observation 55887ddf-8fdc-4a07-8e65-be6bac09fcc9 · outbound

This paper cites Bell, and Chris Volinsky.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Bell, and Chris Volinsky

Reference 20

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Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unresolved cited work

Reference 21

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Observation dfccc6d5-df66-4b86-874b-200d3c140b40 · outbound

This paper cites TEA: A sequential recommendation framework via temporally evolving aggregations.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation TEA: A sequential recommendation framework via temporally evolving aggregations

Reference 22

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Observation 389938b1-cfb7-45c7-8a4f-2e87e7086ab2 · outbound

This paper cites Heterogeneous multidomain recommender system through adversarial learning.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Heterogeneous multidomain recommender system through adversarial learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.176647Z

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Observation 9dcc0c1d-74de-4fd1-99be-e12b9c370989 · outbound

This paper cites K-plet recurrent neural networks for sequential recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation K-plet recurrent neural networks for sequential recommendation

Reference 24

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Observation b7d99a87-0469-4e38-9a94-6869aed92819 · outbound

This paper cites Selfgnn: Self-supervised graph neural networks for sequential recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Selfgnn: Self-supervised graph neural networks for sequential recommendation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.174853Z

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Observation 9d692231-7a07-4d9e-a02c-92ee5f248c0c · outbound

This paper cites Ultragcn: Ultra simplification of graph convolutional networks for recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Ultragcn: Ultra simplification of graph convolutional networks for recommendation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.209819Z

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Observation 962c5184-6caf-4748-897e-d72723428f46 · outbound

This paper cites Factorizing personalized markov chains for next-basket recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Factorizing personalized markov chains for next-basket recommendation

Reference 27

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Observation c2aa73d4-e82e-4936-bb84-39764905ee0b · outbound

This paper cites Unbi- ased recommender learning from missing-not-at-random implicit feedback.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unbi- ased recommender learning from missing-not-at-random implicit feedback

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.211717Z

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Observation da55d259-8009-4872-a8ff-917ed878d6b5 · outbound

This paper cites Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Bert4rec: Sequential recommendation with bidirectional encoder representations from transformer

Reference 29

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Observation bc94f551-6cb7-4f1e-893e-15c55819a44c · outbound

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Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Unresolved cited work

Reference 30

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Observation 2b999b86-2154-4ac4-9987-a84452c71fbc · outbound

This paper cites Graph Convolutional Matrix Completion.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Graph Convolutional Matrix Completion

Reference 31

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Source-reported events for the cited work

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Observation 470d9c06-2f88-4d97-8b76-f02005d52df8 · outbound

This paper cites LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-22T19:25:03.724524Z

Source-reported events for the cited work

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Observation 8dbb45fb-4da3-41b4-8980-3b037963051b · outbound

This paper cites Neural graph collabora- tive filtering.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Neural graph collabora- tive filtering

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.166617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2073dfca-3683-4919-acab-2c31484561d2 · outbound

This paper cites A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation

Reference 34

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Observation 2586ed81-e9c5-46ae-b8d8-107ff5460ece · outbound

This paper cites Session-based recommendation with graph neural networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Session-based recommendation with graph neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.159278Z

Source-reported events for the cited work

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Observation fc6e2921-4ff9-48a1-9a14-4e2a3b397642 · outbound

This paper cites Enhancing robustness in implicit feedback recom- mender systems with subgraph contrastive learning.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Enhancing robustness in implicit feedback recom- mender systems with subgraph contrastive learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.201287Z

Source-reported events for the cited work

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Observation 693b5eab-5398-418a-82ff-d5a6e40776aa · outbound

This paper cites Re- drec: Relation and dynamic aware graph convolutional network for sequential recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Re- drec: Relation and dynamic aware graph convolutional network for sequential recommendation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.157476Z

Source-reported events for the cited work

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Observation c3e12c13-c107-41fd-b35b-e9cca5e58802 · outbound

This paper cites Denoised graph collaborative filtering via neighborhood similarity and dynamic thresholding.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Denoised graph collaborative filtering via neighborhood similarity and dynamic thresholding

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.155598Z

Source-reported events for the cited work

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Observation 3a5c915c-a132-43d4-9291-361e900aaa9f · outbound

This paper cites Dynamic graph neural networks for sequential recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Dynamic graph neural networks for sequential recommendation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.153664Z

Source-reported events for the cited work

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Observation ed8190ef-8829-4342-bbe0-f37235024757 · outbound

This paper cites Dis- entangling long and short-term interests for recommendation.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Dis- entangling long and short-term interests for recommendation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.205646Z

Source-reported events for the cited work

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Observation f8f3f30f-f2b5-4f84-9bbd-aab287556c57 · outbound

This paper cites Incorporating price into recom- mendation with graph convolutional networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Incorporating price into recom- mendation with graph convolutional networks

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.151609Z

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Observation 5ba7bcb3-b93b-418e-b859-718d3ee8f4ef · outbound

This paper cites Graph neural networks: A review of methods and applica- tions.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Graph neural networks: A review of methods and applica- tions

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.163047Z

Source-reported events for the cited work

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Observation db3463fc-1c9d-4713-9f70-ea397d044c9c · outbound

This paper cites Centrality- based and similarity-based neighborhood extension in graph neural networks.

Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation Centrality- based and similarity-based neighborhood extension in graph neural networks

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T19:25:04.164767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.