Paper Citation Record · LEDGER

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2505.19473.

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

pith.paper-citation-record.v1
2505.19473 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:18:45.726648Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T03:47:08.208082Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T03:50:52.056899Z

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy40
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8e2e7850-8123-4a77-acc8-efe907d507fe · outbound

This paper cites Learning optimal and fair decision trees for non-discriminative decision-making.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Learning optimal and fair decision trees for non-discriminative decision-making

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:58.343410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c40a5a91-df73-4bbe-a581-7b03850cdde9 · outbound

This paper cites Fairness without demographic data: A survey of approaches.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairness without demographic data: A survey of approaches

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:58.043930Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 03b26887-53ee-4ea9-b810-b186b1885715 · outbound

This paper cites Tallrec: An effective and efficient tuning framework to align large language model with recommendation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Tallrec: An effective and efficient tuning framework to align large language model with recommendation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:38.862871Z

Source-reported events for the cited work

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Observation 0a062610-63ff-437f-ab71-cd0b89ea099b · outbound

This paper cites Compositional fairness constraints for graph embeddings.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Compositional fairness constraints for graph embeddings

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:57.760735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c49e1f5-3189-4b9d-bb23-2ec12036a698 · outbound

This paper cites Universitat Pompeu Fabra, 2009.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Universitat Pompeu Fabra, 2009

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:57.446030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19aa0f5f-f6fd-4b9b-af82-4d0744573907 · outbound

This paper cites Fairness without demographics through knowledge distillation.Advances in Neural Information Processing Systems, 35:19152–19164, 2022.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairness without demographics through knowledge distillation.Advances in Neural Information Processing Systems, 35:19152–19164, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:57.179095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3349e9be-0230-41cc-a215-33400f4858f1 · outbound

This paper cites Improving recommendation fairness via data augmentation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Improving recommendation fairness via data augmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:56.940563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d90ff0bc-97c4-4d58-aea1-77d6be512f4d · outbound

This paper cites Club: A contrastive log-ratio upper bound of mutual information.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Club: A contrastive log-ratio upper bound of mutual information

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:56.599647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f3366a5b-ee36-4208-888c-28aa11fa7d88 · outbound

This paper cites Flexibly fair representation learning by disentanglement.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Flexibly fair representation learning by disentanglement

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:56.343274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0063d7ba-d232-47b9-8532-13f8e587a31b · outbound

This paper cites Environment inference for invariant learning.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Environment inference for invariant learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:56.051479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e56fa0c8-fee1-4af9-bcfc-47cc244fbef0 · outbound

This paper cites Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:40.007747Z

Source-reported events for the cited work

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Observation 409df85c-d57d-4df8-9b13-ad93ee7c8446 · outbound

This paper cites Maximum likelihood estimation of observer error-rates using the em algorithm.Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1):20–28, 1979.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Maximum likelihood estimation of observer error-rates using the em algorithm.Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1):20–28, 1979

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:40.160450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f648cf7f-f865-4b91-adc1-5cb04afa2972 · outbound

This paper cites Fairness through awareness.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairness through awareness

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:55.721383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f0da54eb-ec17-471b-93c6-ea67a7555263 · outbound

This paper cites Controllable guarantees for fair outcomes via contrastive information estimation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Controllable guarantees for fair outcomes via contrastive information estimation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:55.398944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ba9effe6-cce1-4a2d-a07d-08d83a245fbe · outbound

This paper cites Equality of opportunity in supervised learning.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Equality of opportunity in supervised learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:40.567677Z

Source-reported events for the cited work

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Observation 2cbdf4bc-bb2e-445d-9473-cde968990838 · outbound

This paper cites The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs The movielens datasets: History and context.Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:40.711521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6ffe20a-188b-4774-99ce-5d3d80f9682e · outbound

This paper cites Fairness without demographics in repeated loss minimization.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairness without demographics in repeated loss minimization

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:55.096676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1b3a704c-bee5-4565-ba3a-d3051c0170d0 · outbound

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

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Lightgcn: Simplifying and powering graph convolution network for recommendation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:41.007372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdc59d78-14ec-40c1-9d7a-0e55b7091c36 · outbound

This paper cites Music personalization at spotify.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Music personalization at spotify

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:54.809055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 37fdcf13-3641-4227-8daf-65404e7622e5 · outbound

This paper cites Ir evaluation methods for retrieving highly relevant documents.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Ir evaluation methods for retrieving highly relevant documents

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:54.479694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6da2e7a3-7b84-425e-83a8-a8459f0d7253 · outbound

This paper cites Fairness without demographics through adversarially reweighted learning.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairness without demographics through adversarially reweighted learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:54.105251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23930144-1d42-4630-b65b-29456048b1fb · outbound

This paper cites Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads.Management science, 65(7): 2966–2981, 2019.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Algorithmic bias? an empirical study of apparent gender-based discrimination in the display of stem career ads.Management science, 65(7): 2966–2981, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:53.840516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b77131c-18b1-4186-b378-7ac426466ca2 · outbound

This paper cites Towards person- alized fairness based on causal notion.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Towards person- alized fairness based on causal notion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:53.498410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 18d1a3bc-fd46-40e5-930f-bf14eba9e5f7 · outbound

This paper cites Llara: Large language-recommendation assistant.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Llara: Large language-recommendation assistant

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:53.272259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7caee01b-7178-4a52-b887-a1043f39a149 · outbound

This paper cites Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Rella: Retrieval-enhanced large language models for lifelong sequential behavior comprehension in recommendation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:52.902592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f8be77ac-50e2-473c-bfd3-e5a1b292cf2e · outbound

This paper cites Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:42.245889Z

Source-reported events for the cited work

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Observation a3526ea1-c501-4ef2-83c6-287944eeb2ac · outbound

This paper cites Visual Classification via Description from Large Language Models.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Visual Classification via Description from Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:42.396007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53a0e6bc-5911-435c-a910-b54ac567a489 · outbound

This paper cites Invariant representations without adversarial training.Advances in neural information processing systems, 31, 2018.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Invariant representations without adversarial training.Advances in neural information processing systems, 31, 2018

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:52.588961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ed604525-b215-496a-b7ef-30303b392b51 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Representation Learning with Contrastive Predictive Coding

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:42.709243Z

Source-reported events for the cited work

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Observation 586f91a7-61ac-440f-952b-def1aba5e19b · outbound

This paper cites A theory of justice.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs A theory of justice

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:52.336974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11876559-3993-4738-a82a-5bb8a0a0cfdf · outbound

This paper cites Representation learning with large language models for recommendation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Representation learning with large language models for recommendation

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:52.065160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 791aede1-670f-4251-91de-fa657c513c08 · outbound

This paper cites BPR: Bayesian Personalized Ranking from Implicit Feedback.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs BPR: Bayesian Personalized Ranking from Implicit Feedback

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:43.149786Z

Source-reported events for the cited work

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Observation febf6536-c0ee-4985-942c-536aa9310503 · outbound

This paper cites Leveraging large language models for multiple choice question answering.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Leveraging large language models for multiple choice question answering

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:51.764199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e0731167-3fab-4e3a-990b-9d33701243d9 · outbound

This paper cites Deep learning from crowds.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Deep learning from crowds

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:51.457660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf7925ec-6f04-4b4b-a613-845eee54d7c6 · outbound

This paper cites Opening the Black Box of Deep Neural Networks via Information.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Opening the Black Box of Deep Neural Networks via Information

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:43.520932Z

Source-reported events for the cited work

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Observation f7c4f069-9cdc-4d77-a788-d7dc7e781b58 · outbound

This paper cites Learning controllable fair representations.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Learning controllable fair representations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:51.219108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a3a621c4-86a7-4872-8fbc-69388a0070e3 · outbound

This paper cites A survey on the fairness of recommender systems.ACM Transactions on Information Systems, 41(3):1–43, 2023.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs A survey on the fairness of recommender systems.ACM Transactions on Information Systems, 41(3):1–43, 2023

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:50.925188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2013786d-dfd1-443c-a13a-34b43b4caa51 · outbound

This paper cites Can small language models be good reasoners for sequential recommendation? InProceedings of the ACM on Web Conference 2024, pages 3876–3887, 2024.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Can small language models be good reasoners for sequential recommendation? InProceedings of the ACM on Web Conference 2024, pages 3876–3887, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:50.623372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 98dd741d-6da7-4bb2-94a6-aa1d4d86da18 · outbound

This paper cites Llmrec: Large language models with graph augmentation for recommendation.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Llmrec: Large language models with graph augmentation for recommendation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:50.295707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7298060-a3d3-4d59-bd7a-55487bfc9643 · outbound

This paper cites Learning fair representations for recommendation: A graph-based perspective.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Learning fair representations for recommendation: A graph-based perspective

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:49.949814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f440982f-bdd8-46fb-a667-263a234cc7a3 · outbound

This paper cites Fair class balancing: Enhancing model fairness without observing sensitive attributes.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fair class balancing: Enhancing model fairness without observing sensitive attributes

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:49.688579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ad8d9b51-8828-4c6d-a14d-4471ebb9d588 · outbound

This paper cites Beyond parity: Fairness objectives for collaborative filtering.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Beyond parity: Fairness objectives for collaborative filtering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:49.358239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation decc28d1-46ac-45e6-bce0-32bace1364d0 · outbound

This paper cites Fair sequential recommendation without user demographics.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fair sequential recommendation without user demographics

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:49.061459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5b637f6d-e88b-482f-ba61-a89b31c54a7b · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T14:18:44.420963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 216c212e-7a98-46d6-9506-f6792262e2ad · outbound

This paper cites Fairlisa: Fair user modeling with limited sensitive attributes information.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fairlisa: Fair user modeling with limited sensitive attributes information

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:48.739564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ee649e4-8158-4e1b-97fc-9188ee4c794c · outbound

This paper cites Fair representation learning for recommendation: A mutual information perspective.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Fair representation learning for recommendation: A mutual information perspective

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:48.486243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f20c4d43-01e9-425d-b851-2d23e9e440a2 · outbound

This paper cites Towards fair classifiers without sensitive attributes: Exploring biases in related features.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Towards fair classifiers without sensitive attributes: Exploring biases in related features

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:48.159086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0954c3b2-36bd-459f-a32e-34e7d8bfbf10 · outbound

This paper cites Adaptive fair representation learning for personalized fairness in recommendations via information alignment.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Adaptive fair representation learning for personalized fairness in recommendations via information alignment

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:47.880149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 332af0e1-986b-432c-99be-d7445a50287e · outbound

This paper cites Collaborative large language model for recommender systems.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Collaborative large language model for recommender systems

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:47.625409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 19356d4d-52a9-42e6-8d63-c17311539c6b · outbound

This paper cites # ! "" #.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs # ! "" #

Reference 50

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:18:46.159589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0da48c19-b872-4642-956d-92050789b1ca · outbound

This paper cites These films areoften associated with a female audienceand suggest a fondness for traditional fairy tales and romance.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs These films areoften associated with a female audienceand suggest a fondness for traditional fairy tales and romance

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:47.350426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ba17c35-6609-4d72-b7f0-f3144f631550 · outbound

This paper cites an unresolved cited work.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs Unresolved cited work

Reference 52

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:18:47.061634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 76151ba3-7d67-483b-8482-fe0b188780c8 · outbound

This paper cites 4.The absence of action-oriented or sci-fi movies, which are often popular among male audiences, is a notable pattern.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs 4.The absence of action-oriented or sci-fi movies, which are often popular among male audiences, is a notable pattern

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:46.778647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cda0f9e2-f5bb-4eed-a4bf-54a0933143a3 · outbound

This paper cites While it’s possible that a male user could have similar tastes, the consistency of these themes and patterns across the annotations suggests that the user is likely a female.

Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs While it’s possible that a male user could have similar tastes, the consistency of these themes and patterns across the annotations suggests that the user is likely a female

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:18:46.473735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 1bed4c9c-d150-4196-b6d8-85a1aa162d77 · inbound

A Survey on Generative Recommendation: Data, Model, and Tasks cites this paper.

A Survey on Generative Recommendation: Data, Model, and Tasks Improving Recommendation Fairness without Sensitive Attributes Using Multi-Persona LLMs

Reference 200

Resolution: verified exact; arxiv_id, observed 2026-05-18T03:50:52.059712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.