Paper Citation Record · LEDGER

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System

As of 16 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2509.05115.

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

pith.paper-citation-record.v1
2509.05115 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:40:37.203517Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

33 of 33 outbound references displayed

verified exact1
verified fuzzy28
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b6c7edfc-cec6-45d2-87c3-2f9900c4a3aa · outbound

This paper cites In- troduction to recommender systems handbook.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System In- troduction to recommender systems handbook

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:42.650892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fdf4e833-6417-4f6c-95a4-cb2f2667730e · outbound

This paper cites Neural graph collaborative filtering.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Neural graph collaborative filtering

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:42.474641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c1f98549-a58e-40d7-b0c6-b24ac5dbec51 · outbound

This paper cites Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Revisiting graph based collaborative filtering: A linear residual graph convolutional network approach

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:42.154378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c6b90eb5-fae9-4772-8a72-ec076ea84e65 · outbound

This paper cites STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:34.511767Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d47d9285-c839-4e2f-9000-92c2042f7097 · outbound

This paper cites Interest-aware message-passing gcn for recommen- dation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Interest-aware message-passing gcn for recommen- dation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:41.993792Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 935389de-b7e6-4897-8044-8b54542c40ef · outbound

This paper cites Task-adaptive neural process for user cold- start recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Task-adaptive neural process for user cold- start recommendation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:41.826290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation da43f07d-0831-4274-8f0c-c595d8ad47c5 · outbound

This paper cites Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-05T05:40:37.438572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d6121af4-8206-4748-a4f9-819380860285 · outbound

This paper cites Con- trastive learning for sequential recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Con- trastive learning for sequential recommendation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:41.661045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8f861245-6ae0-4be5-bc02-d8226a698b71 · outbound

This paper cites Self-supervised graph learning for recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Self-supervised graph learning for recommendation

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:41.440194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 540315aa-1b47-43ab-9239-943b20bfb077 · outbound

This paper cites Hypergraph contrastive collaborative filtering.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Hypergraph contrastive collaborative filtering

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:41.184242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2545efb5-5003-4c84-a231-bd867e438d4b · outbound

This paper cites LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:35.203703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94337cdf-1e9c-444e-b559-9e86970e6c51 · outbound

This paper cites Improving graph collaborative filtering with neighborhood-enriched contrastive learning.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Improving graph collaborative filtering with neighborhood-enriched contrastive learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:40.973143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 791abbe7-f5dc-46af-80ea-c2a1474569b9 · outbound

This paper cites Are graph augmen- tations necessary? simple graph contrastive learning for recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Are graph augmen- tations necessary? simple graph contrastive learning for recommendation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:40.779702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 40c3e826-fd28-4697-bbca-20b8cd72f1b0 · outbound

This paper cites Xsimgcl: Towards extremely simple graph contrastive learning for recom- mendation.IEEE Transactions on Knowledge and Data Engineering, 2023.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Xsimgcl: Towards extremely simple graph contrastive learning for recom- mendation.IEEE Transactions on Knowledge and Data Engineering, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:40.547090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f3f4196-b0e7-41b8-b43d-c34d31fa92ae · outbound

This paper cites T-gcn: A tem- poral graph convolutional network for traffic prediction.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System T-gcn: A tem- poral graph convolutional network for traffic prediction

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:40.326608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9825a74e-b20f-4961-b22d-7ca5b16e3719 · outbound

This paper cites Predicting traf- fic propagation flow in urban road network with multi- graph convolutional network.Complex&Intelligent Sys- tems, 10(1):23–35, 2024.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Predicting traf- fic propagation flow in urban road network with multi- graph convolutional network.Complex&Intelligent Sys- tems, 10(1):23–35, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:40.106159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3b35b0dd-abfc-4b21-80f2-0b40c8bd6d80 · outbound

This paper cites Towards rep- resentation alignment and uniformity in collaborative fil- tering.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Towards rep- resentation alignment and uniformity in collaborative fil- tering

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.953783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bbb531ca-b17f-426a-bffd-0e8bea196117 · outbound

This paper cites Self- supervised multi-channel hypergraph convolutional net- work for social recommendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Self- supervised multi-channel hypergraph convolutional net- work for social recommendation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.806064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d1fb030a-b35d-4b39-b5fe-54bc0fdc9be8 · outbound

This paper cites Graph neural network recommendation algorithm based on improved dual tower model.Scientific Reports, 14(1):3853, 2024.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Graph neural network recommendation algorithm based on improved dual tower model.Scientific Reports, 14(1):3853, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.668367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 55e080f4-e712-4334-9eb6-3a332a966cd5 · outbound

This paper cites an unresolved cited work.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-05T05:40:39.551370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 481aa4bd-9a54-40c9-89a8-ba36ea9e68c0 · outbound

This paper cites S3-rec: Self-supervised learning for sequential rec- ommendation with mutual information maximization.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System S3-rec: Self-supervised learning for sequential rec- ommendation with mutual information maximization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.396805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd559146-ef55-4349-977d-b4adcad6158b · outbound

This paper cites Graph contrastive learning with augmentations.Advances in neural infor- mation processing systems, 33:5812–5823, 2020.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Graph contrastive learning with augmentations.Advances in neural infor- mation processing systems, 33:5812–5823, 2020

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.257415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 405fb372-bd70-4af4-a141-8bee10d76fb8 · outbound

This paper cites Contrastive learning with stronger augmentations.IEEE transactions on pat- tern analysis and machine intelligence, 45(5):5549–5560, 2022.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Contrastive learning with stronger augmentations.IEEE transactions on pat- tern analysis and machine intelligence, 45(5):5549–5560, 2022

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:39.079065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f44f920b-7ec3-48c8-87bd-cb62866d7b7f · outbound

This paper cites Weakly supervised contrastive learning.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Weakly supervised contrastive learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.946575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32ca2f96-d06d-49e8-9c66-a22c8575bfa6 · outbound

This paper cites Gnncl: A graph neural network recommendation model based on contrastive learning.Neural Processing Letters, 56(2):45, 2024.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Gnncl: A graph neural network recommendation model based on contrastive learning.Neural Processing Letters, 56(2):45, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.812293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e6a61f4f-6ecb-4c13-b977-7638a283ed75 · outbound

This paper cites Generative-contrastive graph learning for recom- mendation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Generative-contrastive graph learning for recom- mendation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.649221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2746b33e-04cc-4f28-8f59-432f30022ec6 · outbound

This paper cites Deep matrix factorization mod- els for recommender systems.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Deep matrix factorization mod- els for recommender systems

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.486656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 272da82d-d15e-4d66-80ba-438d734140da · outbound

This paper cites Graph Convolutional Matrix Completion.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Graph Convolutional Matrix Completion

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-05T05:40:36.788427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d087b8f-9ca0-4297-968b-8701a875dcc0 · outbound

This paper cites Embarrassingly shallow autoencoders for sparse data.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Embarrassingly shallow autoencoders for sparse data

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.314047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fc334ee5-0731-4721-b537-0c2e52ba2d84 · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommen- dation.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Lightgcn: Simplifying and powering graph convolution network for recommen- dation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:38.131281Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6cab3d8b-dd72-4c5c-b08c-700585a75a34 · outbound

This paper cites Efficient neural matrix factorization without sampling for recommendation.ACM Transac- tions on Information Systems (TOIS), 38(2):1–28, 2020.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Efficient neural matrix factorization without sampling for recommendation.ACM Transac- tions on Information Systems (TOIS), 38(2):1–28, 2020

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:37.934274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b307a960-f508-40da-844d-7dd156a4c1e0 · outbound

This paper cites Simplex: A simple and strong baseline for collaborative filtering.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Simplex: A simple and strong baseline for collaborative filtering

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:37.772107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f7e0e807-8ec0-49df-b0a3-4d103ff605ae · outbound

This paper cites Recbole: Towards a unified, comprehensive and efficient framework for recommenda- tion algorithms.

Hybrid Matrix Factorization Based Graph Contrastive Learning for Recommendation System Recbole: Towards a unified, comprehensive and efficient framework for recommenda- tion algorithms

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:40:37.594138Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.