Paper Citation Record · LEDGER

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples

As of 5 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2509.03187.

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

pith.paper-citation-record.v1
2509.03187 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:11:38.799430Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+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

24 of 24 outbound references displayed

verified exact0
verified fuzzy3
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 83217e79-3837-41c0-9fe8-00bbb761d79f · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.658495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c7047bfa-8bde-4386-9f5c-9188f8709583 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.194179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 242d854f-a53f-4461-bb08-5a041874dd42 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.324033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b93e670-751d-4b1b-805c-f2ca2bdaf584 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.384394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f4d25ec-58cf-493e-b97c-de297330f140 · outbound

This paper cites In Proceedings of the 1st workshop on deep learning for recommender systems.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples In Proceedings of the 1st workshop on deep learning for recommender systems

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.248723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee6fad4e-4ef5-4b69-a300-00c792fcdeb3 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.597636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e0938a28-8847-4de6-be87-caace92c71de · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.578951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 15fe7ce5-1e20-4f20-8a1a-3a8d3f5f5440 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.412737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1c4090b-bba4-40c3-a6a3-82f01c139a43 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.562239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 0a60ba76-f38c-4eb8-893e-e27e3c449000 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.548556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b203a3a7-e515-47d8-932d-2d1318d168a3 · outbound

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

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.601747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab5c9358-4a5f-48b5-9cd3-07eeb862f2ee · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.926959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9723501d-804a-4663-882b-ec97a54e35f6 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:38.044577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e801408a-0e35-4119-bcea-712fe24c0d81 · outbound

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

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:37.837490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3432fd1b-dd52-43be-b698-cffe0f6cd7f5 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.487741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 17a1e714-cf0f-49e9-9ed6-a4f156e28be6 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 17

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T11:11:39.168313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6ffa4612-9fd6-4f08-b0d8-ae2220f374be · outbound

This paper cites In 2016 IEEE 16th International Conference on Data Mining (ICDM).

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples In 2016 IEEE 16th International Conference on Data Mining (ICDM)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:11:39.526253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 789e3417-8657-4a25-ada0-c70725eb5b3e · outbound

This paper cites Why should i trust you?.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Why should i trust you?

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:11:39.512167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9fb25691-fc89-4ab6-8dfa-74763e6d2adb · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.460317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c14a46a6-02fa-4d08-82c0-f673ccfbf536 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.447079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5016d397-c817-4828-a1e0-a5a55e4b0b75 · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.473815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3d092b7c-0f35-4ef3-8978-0f5127ac1e28 · outbound

This paper cites Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:38.513278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75496518-3745-456f-a082-f9231740bf6b · outbound

This paper cites an unresolved cited work.

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:11:39.433702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1c70c86d-d2f2-424f-9711-124d32b36f14 · outbound

This paper cites In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Enhancing Interpretability and Effectiveness in Recommendation with Numerical Features via Learning to Contrast the Counterfactual samples In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:11:39.645664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.