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Paper Citation Record · LEDGER

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models

As of 18 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 0 inbound Pith citation observations for arXiv:2509.07319.

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

pith.paper-citation-record.v1
2509.07319 v1

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 63 of 63 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation e46f61dc-0d7a-4d79-8e87-3156298a5e0f · outbound

This paper cites In: Proceedings of the 26th International Con- ference on World Wide Web, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 26th International Con- ference on World Wide Web, pp

Reference 1

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Observation cc0275a8-1180-4d64-991e-fa0bb307b702 · outbound

This paper cites In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp

Reference 2

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Observation 1c5dd02e-1945-4fa6-b287-661dba4cc01b · outbound

This paper cites In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowl- edge Discovery and Data Mining, pp

Reference 3

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Observation fa8532d2-dc8c-49d6-af40-f3d9a538c6f4 · outbound

This paper cites In: Proceedings of the 40th International ACM 14 SIGIR Conference on Research and Develop- ment in Information Retrieval, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 40th International ACM 14 SIGIR Conference on Research and Develop- ment in Information Retrieval, pp

Reference 4

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Observation 1cad0b05-40bc-4976-8c61-56d2bb7966c7 · outbound

This paper cites DeepFM: A Factorization-Machine based Neural Network for CTR Prediction.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 5

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Observation a6f4fc0d-6cf2-4d8b-9202-063cc4b61174 · outbound

This paper cites xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Reference 6

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Unresolved cited work

Reference 7

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Observation 829116d6-9072-4a6d-9d7d-268e31d139ee · outbound

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

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 8

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Observation 0be21f5d-a3dd-4dbb-86e1-95355f0044ab · outbound

This paper cites In: International Symposium on Ubiquitious Computing Systems, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: International Symposium on Ubiquitious Computing Systems, pp

Reference 9

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Observation a3f8711a-95bf-45e1-97ef-ae4ed02bfe9f · outbound

This paper cites In: Proceedings of the 15th Inter- national Conference on Intelligent User Inter- faces, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 15th Inter- national Conference on Intelligent User Inter- faces, pp

Reference 10

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This paper cites In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp

Reference 11

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This paper cites In: 2023 5th Interna- tional Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: 2023 5th Interna- tional Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp

Reference 12

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This paper cites In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp

Reference 13

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Observation 19fccdc7-d483-4388-a5c9-8928e62a65ef · outbound

This paper cites In: Proceedings of the 14th ACM Conference on Recommender Systems.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 14th ACM Conference on Recommender Systems

Reference 14

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This paper cites In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 15

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the ACM Web Conference

Reference 16

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Observation 3dee3d20-cbf1-4067-9122-b7b280264e23 · outbound

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models 2861–2868 (2020)

Reference 17

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This paper cites In: Proceedings of the European Conference on Computer Vision 15 (ECCV) (2018).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the European Conference on Computer Vision 15 (ECCV) (2018)

Reference 18

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Observation 70605b04-ace7-45e6-8a27-8c7255672516 · outbound

This paper cites Three scenarios for continual learning.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Three scenarios for continual learning

Reference 19

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This paper cites A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks

Reference 20

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This paper cites Class-incremental learning: survey and performance evaluation on image classification.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Class-incremental learning: survey and performance evaluation on image classification

Reference 21

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Observation 6b63b2b2-763c-4bb8-8681-2370e336dd50 · outbound

This paper cites Incremental Learning of Object Detectors without Catastrophic Forgetting.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Incremental Learning of Object Detectors without Catastrophic Forgetting

Reference 22

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Scott, D., Bel, N., Zong, C

Reference 23

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: ICLR (2022)

Reference 24

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Recent Advances of Continual Learning in Computer Vision: An Overview

Reference 25

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Neurocomputing469, 28–51 (2022)

Reference 26

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Reference 27

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 30th ACM International Conference on Information & Knowledge Manage- ment

Reference 29

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Causal Incremental Graph Convolution for Recommender System Retraining

Reference 30

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 2nd Interna- tional Workshop on Deep Multimodal Gener- ation and Retrieval

Reference 31

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization

Reference 32

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination

Reference 33

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MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the AAAI Symposium Series, vol

Reference 34

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This paper cites RecSys ’22, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models RecSys ’22, pp

Reference 35

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Observation 8c9c2649-a37c-4bb4-92d8-4f09f6e35edf · outbound

This paper cites A Practical Incremental Method to Train Deep CTR Models.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models A Practical Incremental Method to Train Deep CTR Models

Reference 36

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bc7bbea0-9d39-4b32-9aff-7e590d533a2c · outbound

This paper cites Incremental Learning for Personalized Recommender Systems.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Incremental Learning for Personalized Recommender Systems

Reference 37

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metadata mismatch
local_arxiv, observed 2026-08-04T22:32:18.173308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 84f28e7e-c7ae-41e3-86cf-890989f7050e · outbound

This paper cites Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Incremental Factorization Machines for Persistently Cold-starting Online Item Recommendation

Reference 38

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local_arxiv, observed 2026-08-04T22:32:18.141108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 3400bcfc-a220-4aba-8f78-02c3053ca878 · outbound

This paper cites In: User Modeling, Adaptation, and Personaliza- tion: 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: User Modeling, Adaptation, and Personaliza- tion: 22nd International Conference, UMAP 2014, Aalborg, Denmark, July 7-11, 2014

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.873910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation cf25c771-5100-436b-89c0-4895e644ae63 · outbound

This paper cites Interna- tional Journal of Computer Vision129(6), 1789–1819 (2021).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Interna- tional Journal of Computer Vision129(6), 1789–1819 (2021)

Reference 40

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 220d9341-f90a-4297-8758-db54bab30b04 · outbound

This paper cites In: Com- puter Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Com- puter Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16, pp

Reference 41

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.847209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 91c0e32f-c2da-4efd-9e5d-e6dff3eff841 · outbound

This paper cites iCaRL: Incremental Classifier and Representation Learning.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models iCaRL: Incremental Classifier and Representation Learning

Reference 42

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5ffcdcdf-1d7f-4608-9657-2e8eccc28713 · outbound

This paper cites Online Continual Learning with Maximally Interfered Retrieval.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Online Continual Learning with Maximally Interfered Retrieval

Reference 43

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metadata mismatch
local_arxiv, observed 2026-08-04T22:32:18.092940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6f477ebc-735b-4da3-aad7-745bcea70b87 · outbound

This paper cites In: Psychology of Learning and Motivation vol.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Psychology of Learning and Motivation vol

Reference 44

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation a7ac7c6f-a152-46c4-87c5-76fde40674ac · outbound

This paper cites In: International Conference on Machine Learn- ing, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: International Conference on Machine Learn- ing, pp

Reference 45

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.800494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f20b3581-eced-450f-90ba-4767bd2b9d79 · outbound

This paper cites Advances in neural information processing systems30(2017).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Advances in neural information processing systems30(2017)

Reference 46

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.783557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation b03bc0dc-9616-4551-b637-92e4676e0868 · outbound

This paper cites In: European Conference on Information Retrieval, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: European Conference on Information Retrieval, pp

Reference 47

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.765939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation f6893595-3023-4a3d-97f7-f0b62ea86dbd · outbound

This paper cites In: Duh, K., Gomez, H., Bethard, S.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Duh, K., Gomez, H., Bethard, S

Reference 48

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 598d393b-b37e-47a5-a6cc-d94a09ac037e · outbound

This paper cites SLMRec: Distilling Large Language Models into Small for Sequential Recommendation.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

Reference 49

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Observation 5258db7d-237a-4ae2-bda1-4a327f9f14a5 · outbound

This paper cites In: Proceedings of the AAAI Conference on Artificial Intelli- gence, vol.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the AAAI Conference on Artificial Intelli- gence, vol

Reference 50

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation bcbffd4a-728f-4d2b-b0cf-760f9b94690c · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence44(7), 3366–3385 (2022) https://doi.org/10.1109/ TPAMI.2021.3057446.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models IEEE Transactions on Pattern Analysis and Machine Intelligence44(7), 3366–3385 (2022) https://doi.org/10.1109/ TPAMI.2021.3057446

Reference 51

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 75de4f2d-2ba0-472b-b020-9beeeb5f3766 · outbound

This paper cites Data-centric Artificial Intelligence: A Survey.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Data-centric Artificial Intelligence: A Survey

Reference 52

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Unavailable: canonical work link unavailable.

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Observation de09efb6-4b69-42c8-9c89-106a1c7df29e · outbound

This paper cites Transactions on Machine Learning Research (2023).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Transactions on Machine Learning Research (2023)

Reference 53

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation afc327f4-bc93-49b5-a8a8-cdac51be7256 · outbound

This paper cites Revisiting Distillation and Incremental Classifier Learning.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Revisiting Distillation and Incremental Classifier Learning

Reference 54

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Unavailable: canonical work link unavailable.

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Observation d9f94b8e-f4b0-4123-95c7-4cf4041bd9ea · outbound

This paper cites In: International Conference on Machine Learn- ing, pp.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: International Conference on Machine Learn- ing, pp

Reference 55

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verified fuzzy
raw_fallback, observed 2026-08-04T22:32:18.713054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 14c8e8b8-d97f-4727-903a-111a91983a38 · outbound

This paper cites Advances in Neural Information Processing Systems32(2019).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Advances in Neural Information Processing Systems32(2019)

Reference 56

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raw_fallback, observed 2026-08-04T22:32:18.696953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 6bad427f-3841-44f5-adf0-b9a80d7127f9 · outbound

This paper cites Dataset Pruning: Reducing Training Data by Examining Generalization Influence.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Dataset Pruning: Reducing Training Data by Examining Generalization Influence

Reference 57

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 74a8cd9e-aa3c-46c6-9732-01a794c8641a · outbound

This paper cites In: Proceedings of the 2022 International Conference on Manage- ment of Data.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models In: Proceedings of the 2022 International Conference on Manage- ment of Data

Reference 58

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Unavailable: canonical work link unavailable.

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Observation 5fa425f8-a47f-4afb-a34f-a2e9ffa92108 · outbound

This paper cites Proceedings of the IEEE86(11), 2278–2324 (1998) https://doi.org/10.1109/5.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Proceedings of the IEEE86(11), 2278–2324 (1998) https://doi.org/10.1109/5

Reference 59

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation e1432970-bd87-4420-bf62-5b6c8dd44556 · outbound

This paper cites Selective and Collaborative Influence Function for Efficient Recommendation Unlearning.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Selective and Collaborative Influence Function for Efficient Recommendation Unlearning

Reference 60

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local_arxiv, observed 2026-08-04T22:32:17.956368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 45d15540-c5e1-4a5f-8783-666c2b48f3d5 · outbound

This paper cites Springer, ??? (2010).

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Springer, ??? (2010)

Reference 61

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raw_fallback, observed 2026-08-04T22:32:18.680825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation c40ff045-53ac-4f05-a03c-65a68f0512f0 · outbound

This paper cites an unresolved cited work.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Unresolved cited work

Reference 62

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unresolved
raw_fallback, observed 2026-08-04T22:32:18.663802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation 11d28b58-ad39-4008-8d9a-6b46e8598a28 · outbound

This paper cites an unresolved cited work.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models Unresolved cited work

Reference 165

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Observation adfde178-d636-434a-8e48-c7f97f0c2e2d · outbound

This paper cites 2360–2369.

MEGG: Replay via Maximally Extreme GGscore in Incremental Learning for Neural Recommendation Models 2360–2369

Reference 2022

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arxiv_id, observed 2026-08-04T22:32:18.536100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

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Pith citing papers

No inbound Pith citation observations are available.