Decoding Consumer Preferences Using Attention-Based Language Models | Paper Citation Record

Observation 7985e28a-6450-409c-a344-430065e26288 · outbound

This paper cites Hedonic prices and implicit markets: product differentiation in pure competition.Journal of Political Economy, 82(1):34–55, 1974.

Decoding Consumer Preferences Using Attention-Based Language Models Hedonic prices and implicit markets: product differentiation in pure competition.Journal of Political Economy, 82(1):34–55, 1974

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.723651Z

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Observation 1b7ae75d-9284-473d-b6f6-1893f275d387 · outbound

This paper cites Machine learning as a tool for hypothesis generation.Quarterly Journal of Economics, 139(2):751–827, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Machine learning as a tool for hypothesis generation.Quarterly Journal of Economics, 139(2):751–827, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.714342Z

Source-reported events for the cited work

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Observation 9f16d8b1-1661-4ac1-b4c2-454259f1cc13 · outbound

This paper cites On the choice of funtional form for hedonic price functions.Review of Economics and Statistics, pages 668–675, 1988.

Decoding Consumer Preferences Using Attention-Based Language Models On the choice of funtional form for hedonic price functions.Review of Economics and Statistics, pages 668–675, 1988

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.704272Z

Source-reported events for the cited work

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

Observation 8e19dd4b-d630-40d2-9365-183d9c6f8a53 · outbound

This paper cites Linguistic regularities in continuous space word representations.

Decoding Consumer Preferences Using Attention-Based Language Models Linguistic regularities in continuous space word representations

Reference 4

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Source-reported events for the cited work

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Observation dc028584-1961-4133-afb9-e2348e133212 · outbound

This paper cites Pre-trained models for natural language processing: A survey.Science China Technological Sciences, 63(10):1872–1897, 2020.

Decoding Consumer Preferences Using Attention-Based Language Models Pre-trained models for natural language processing: A survey.Science China Technological Sciences, 63(10):1872–1897, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.685612Z

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

Observation 0a4a6c9f-38a9-4f37-8d31-286073f8bed4 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Decoding Consumer Preferences Using Attention-Based Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

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Source-reported events for the cited work

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Observation 88c86b0a-0407-4e0c-a137-7d25c6f11226 · outbound

This paper cites The impact of machine learning on economics.

Decoding Consumer Preferences Using Attention-Based Language Models The impact of machine learning on economics

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Source-reported events for the cited work

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Observation 70281575-f3e4-4cbb-a61b-c14b0e168e2e · outbound

This paper cites How to sell a data set? pricing policies for data monetization.Information Systems Research, 32(4):1281–1297, 2021.

Decoding Consumer Preferences Using Attention-Based Language Models How to sell a data set? pricing policies for data monetization.Information Systems Research, 32(4):1281–1297, 2021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.666406Z

Source-reported events for the cited work

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Observation 683176b4-9da2-458e-9318-b97e621f69d3 · outbound

This paper cites Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods.Journal of Business Research, 144:93–106, 2022.

Decoding Consumer Preferences Using Attention-Based Language Models Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods.Journal of Business Research, 144:93–106, 2022

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.656242Z

Source-reported events for the cited work

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

Observation ba38b528-90d5-4c5c-b1d1-1c437f543166 · outbound

This paper cites Pathways for design research on artificial intelligence.Information Systems Research, 35(2):441–459, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Pathways for design research on artificial intelligence.Information Systems Research, 35(2):441–459, 2024

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.645717Z

Source-reported events for the cited work

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This paper cites an unresolved cited work.

Decoding Consumer Preferences Using Attention-Based Language Models Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-06T14:51:53.635678Z

Source-reported events for the cited work

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

Observation c23aab76-416d-495c-b0f4-5cb62e80e8ef · outbound

This paper cites Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

Decoding Consumer Preferences Using Attention-Based Language Models Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 12

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Observation 053ee031-5c76-4f39-9ee7-148eede06974 · outbound

This paper cites Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019.

Decoding Consumer Preferences Using Attention-Based Language Models Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019

Reference 13

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Observation 1ca13ae1-9f20-49f4-8ec8-bba270e9ad63 · outbound

This paper cites Highly accurate protein structure prediction with alphafold.Nature, 596(7873):583–589, 2021.

Decoding Consumer Preferences Using Attention-Based Language Models Highly accurate protein structure prediction with alphafold.Nature, 596(7873):583–589, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:52.929726Z

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Observation b1340c48-d469-4226-bae2-cbb0d3c61b0f · outbound

This paper cites TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation.

Decoding Consumer Preferences Using Attention-Based Language Models TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Reference 15

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Observation 0fbaad12-f13c-4414-b1c7-c87f6a12aa33 · outbound

This paper cites Large language model in creative work: The role of collaboration modality and user expertise.Management Science, 70(12):3450–3472, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Large language model in creative work: The role of collaboration modality and user expertise.Management Science, 70(12):3450–3472, 2024

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.609881Z

Source-reported events for the cited work

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Observation f18f9c63-5c5e-4277-81ec-17e6fb59aae2 · outbound

This paper cites Automated analysis of changes in privacy policies: A structured self-attentive sentence embedding approach.MIS Quarterly, 48(4), 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Automated analysis of changes in privacy policies: A structured self-attentive sentence embedding approach.MIS Quarterly, 48(4), 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.600734Z

Source-reported events for the cited work

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Observation f105a4f2-99ad-44e7-a229-d19c6fa52d1f · outbound

This paper cites Can chatgpt perform a grounded theory approach to do risk analysis? an empirical study.Journal of Management Information Systems, 41(4):982–1015, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Can chatgpt perform a grounded theory approach to do risk analysis? an empirical study.Journal of Management Information Systems, 41(4):982–1015, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.590798Z

Source-reported events for the cited work

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

Observation b35e786b-3fda-411e-81ed-6d61885b93a9 · outbound

This paper cites Predicting instructor performance in online education: An interpretable hierarchical transformer with contextual attention.Information Systems Research, 2025.

Decoding Consumer Preferences Using Attention-Based Language Models Predicting instructor performance in online education: An interpretable hierarchical transformer with contextual attention.Information Systems Research, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.580128Z

Source-reported events for the cited work

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Observation ec31b2ea-5b97-407c-a339-96e5afd74bb5 · outbound

This paper cites Text as data.Journal of Economic Literature, 57(3):535–574, 2019.

Decoding Consumer Preferences Using Attention-Based Language Models Text as data.Journal of Economic Literature, 57(3):535–574, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.569451Z

Source-reported events for the cited work

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Observation 82dd73cd-bd48-4009-b526-76dffdc8af23 · outbound

This paper cites Moe, Oded Netzer, and David A.

Decoding Consumer Preferences Using Attention-Based Language Models Moe, Oded Netzer, and David A

Reference 21

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Source-reported events for the cited work

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

Observation bd1733e2-9ac4-4448-942d-0a2826efceba · outbound

This paper cites Text algorithms in economics.Annual Review of Economics, 15:659–688, 2023.

Decoding Consumer Preferences Using Attention-Based Language Models Text algorithms in economics.Annual Review of Economics, 15:659–688, 2023

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Source-reported events for the cited work

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Observation a837d041-0722-4ce9-bab5-3a3e5721e9ba · outbound

This paper cites Chatgpt for textual analysis? how to use generative llms in accounting research.Management Science, 71(1):123–145, 2025.

Decoding Consumer Preferences Using Attention-Based Language Models Chatgpt for textual analysis? how to use generative llms in accounting research.Management Science, 71(1):123–145, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.543494Z

Source-reported events for the cited work

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

Observation f93562e9-d58b-43c7-a745-25f2a93755ac · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models The impact of increase in minimum wages on consumer perceptions of service: A transformer model of online restaurant reviews.Marketing Science, 40(5):985–1004, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.534684Z

Source-reported events for the cited work

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

Observation 03dd6360-7d77-4399-89cc-6ffd1bbaa733 · outbound

This paper cites Getting personal: A deep learning artifact for text-based measurement of personality.Information Systems Research, 34(1):194–222, 2023.

Decoding Consumer Preferences Using Attention-Based Language Models Getting personal: A deep learning artifact for text-based measurement of personality.Information Systems Research, 34(1):194–222, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.526065Z

Source-reported events for the cited work

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

Observation 04c073a2-52d1-419a-956b-10c58235c7d1 · outbound

This paper cites an unresolved cited work.

Decoding Consumer Preferences Using Attention-Based Language Models Unresolved cited work

Reference 26

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Source-reported events for the cited work

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

Observation 5913a1f1-abc5-41dc-bf19-5bc387aaef43 · outbound

This paper cites Consumer risk preferences elicitation from large language models.

Decoding Consumer Preferences Using Attention-Based Language Models Consumer risk preferences elicitation from large language models

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Source-reported events for the cited work

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Observation 85d8a8e3-6254-4a11-bcc3-8c9da3d72ef2 · outbound

This paper cites Fin-alice: Artificial linguistic intelligence causal econometrics.

Decoding Consumer Preferences Using Attention-Based Language Models Fin-alice: Artificial linguistic intelligence causal econometrics

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Source-reported events for the cited work

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Observation e5d3fc4b-9760-49ac-bc99-4b554158a313 · outbound

This paper cites an unresolved cited work.

Decoding Consumer Preferences Using Attention-Based Language Models Unresolved cited work

Reference 29

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Source-reported events for the cited work

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

Observation 62df6515-2b38-4344-8d5a-e6104e49183f · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Deep learning in marketing: a review and research agenda.Artificial Intelligence in Marketing, pages 239–271, 2023

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Source-reported events for the cited work

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Observation 181faf40-217b-481b-84ca-bcd830474cab · outbound

This paper cites Large Language Models for Market Research: A Data-augmentation Approach.

Decoding Consumer Preferences Using Attention-Based Language Models Large Language Models for Market Research: A Data-augmentation Approach

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Observation e3d17411-2b31-430c-9ae8-13327d787566 · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Conversation analytics: Can machines read between the lines in real-time strategic conversations?Information Systems Research, 36(1):440–455, 2025

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Source-reported events for the cited work

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Observation 1f08f630-0316-4d23-8e3c-ae375aeeec48 · outbound

This paper cites Machine learning methods that economists should know about.

Decoding Consumer Preferences Using Attention-Based Language Models Machine learning methods that economists should know about

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Source-reported events for the cited work

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Observation f4cf316d-58c5-4100-b258-1948e37694f1 · outbound

This paper cites Machine learning for demand estimation in long tail markets.

Decoding Consumer Preferences Using Attention-Based Language Models Machine learning for demand estimation in long tail markets

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

Observation be5a75b8-64ca-4e16-9da2-e0cc5a4d3135 · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Mobilizing conceptual spaces: How word embedding models can inform measurement and theory within organization science.Organization Science, 35(3):788–814, 2024

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Source-reported events for the cited work

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

Observation 5bbd4ed2-04d2-4142-bc80-04f4816453a0 · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Can ai language models replace human participants? Trends in Cognitive Sciences, 27(7):597–600, 2023

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Observation fbd897b4-940d-40bf-9df1-0239be4c7045 · outbound

This paper cites Large Language Models Can Be Used to Estimate the Latent Positions of Politicians.

Decoding Consumer Preferences Using Attention-Based Language Models Large Language Models Can Be Used to Estimate the Latent Positions of Politicians

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Observation 68bbfb73-6f17-4600-840b-6b1658567e8d · outbound

This paper cites Frontiers: Can large language models capture human preferences? Marketing Science, 43(4):709–722, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Frontiers: Can large language models capture human preferences? Marketing Science, 43(4):709–722, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.420038Z

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

Observation 2ce5bf85-5d2a-47c2-ba6c-bf5cdb11632b · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice

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Observation 400e7aa6-34a6-4a3f-b370-f9b617564e32 · outbound

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Decoding Consumer Preferences Using Attention-Based Language Models Zoom in: An introduction to circuits.Distill, 5(3):e00024–001, 2020

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Observation 84959d89-cfba-44fc-9b86-5941c4557d11 · outbound

This paper cites Toy Models of Superposition.

Decoding Consumer Preferences Using Attention-Based Language Models Toy Models of Superposition

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.009055Z

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Observation 082190d1-eb0b-4eae-ab8b-2b75ff9c68a4 · outbound

This paper cites Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet.

Decoding Consumer Preferences Using Attention-Based Language Models Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet

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Observation 8b45233f-a25f-4d72-867c-c669ae60f05e · outbound

This paper cites Counterfactual explanations and algorithmic recourses for machine learning: A review.ACM Computing Surveys, 56(12):1–42, 2024.

Decoding Consumer Preferences Using Attention-Based Language Models Counterfactual explanations and algorithmic recourses for machine learning: A review.ACM Computing Surveys, 56(12):1–42, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.397569Z

Source-reported events for the cited work

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

Observation 8692d0b3-ed92-4c85-8de2-4974a6f8cac0 · outbound

This paper cites Finetuned Language Models Are Zero-Shot Learners.

Decoding Consumer Preferences Using Attention-Based Language Models Finetuned Language Models Are Zero-Shot Learners

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.018013Z

Source-reported events for the cited work

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Observation 073ad9c1-b4ba-466e-b898-f30bdcacc004 · outbound

This paper cites Estimating Wage Disparities Using Foundation Models.

Decoding Consumer Preferences Using Attention-Based Language Models Estimating Wage Disparities Using Foundation Models

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Source-reported events for the cited work

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

Observation 7475fb18-ccc3-47e0-bc23-188f0c26c088 · outbound

This paper cites Towards general text embeddings with multi-stage contrastive learning, 2023.

Decoding Consumer Preferences Using Attention-Based Language Models Towards general text embeddings with multi-stage contrastive learning, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.023927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fc54faf-0217-45d5-a1ca-bc2278dcaeb1 · outbound

This paper cites Word representations: a simple and general method for semi-supervised learning.

Decoding Consumer Preferences Using Attention-Based Language Models Word representations: a simple and general method for semi-supervised learning

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.381169Z

Source-reported events for the cited work

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

Observation e8781ec7-2661-4158-9190-604fb014858f · outbound

This paper cites Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning.

Decoding Consumer Preferences Using Attention-Based Language Models Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.029640Z

Source-reported events for the cited work

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Observation f85567fd-965a-441a-9c1b-3775398a55c7 · outbound

This paper cites On the Sentence Embeddings from Pre-trained Language Models.

Decoding Consumer Preferences Using Attention-Based Language Models On the Sentence Embeddings from Pre-trained Language Models

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Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.032639Z

Source-reported events for the cited work

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Observation 4e405069-d4fa-408b-82ed-b52cdca528c0 · outbound

This paper cites Machine learning: an applied econometric approach.Journal of Economic Perspectives, 31(2):87–106, 2017.

Decoding Consumer Preferences Using Attention-Based Language Models Machine learning: an applied econometric approach.Journal of Economic Perspectives, 31(2):87–106, 2017

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.371371Z

Source-reported events for the cited work

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Observation aa6058f0-8f1c-4709-92c6-3d3a59bd6d04 · outbound

This paper cites Machine learning and structural econometrics: contrasts and synergies.The Econometrics Journal, 23(3):S81–S124, 2020.

Decoding Consumer Preferences Using Attention-Based Language Models Machine learning and structural econometrics: contrasts and synergies.The Econometrics Journal, 23(3):S81–S124, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.362748Z

Source-reported events for the cited work

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

Observation cd843e49-208b-4b50-86b7-8a4524101506 · outbound

This paper cites Semi-nonparametric maximum likelihood estimation.

Decoding Consumer Preferences Using Attention-Based Language Models Semi-nonparametric maximum likelihood estimation

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.353128Z

Source-reported events for the cited work

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

Observation f96d0fbf-d868-45b4-8382-6da5401e4331 · outbound

This paper cites Convergence rates of snp density estimators.Econometrica, pages 719–727, 1996.

Decoding Consumer Preferences Using Attention-Based Language Models Convergence rates of snp density estimators.Econometrica, pages 719–727, 1996

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.343590Z

Source-reported events for the cited work

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

Observation 6410c994-a7e9-4f81-8975-d6c25376e622 · outbound

This paper cites Qualitative and asymptotic performance of snp density estimators.

Decoding Consumer Preferences Using Attention-Based Language Models Qualitative and asymptotic performance of snp density estimators

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.333806Z

Source-reported events for the cited work

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

Observation ab7900f9-0418-4acd-931e-173294417ba9 · outbound

This paper cites Deep Learning for Individual Heterogeneity.

Decoding Consumer Preferences Using Attention-Based Language Models Deep Learning for Individual Heterogeneity

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.050153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0701831b-6c2a-44ab-9086-4f557e90bcdc · outbound

This paper cites Auctions: an introduction.Journal of Economic Surveys, 10(4):367–420, 1996.

Decoding Consumer Preferences Using Attention-Based Language Models Auctions: an introduction.Journal of Economic Surveys, 10(4):367–420, 1996

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.323991Z

Source-reported events for the cited work

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

Observation 64838c5f-e4b3-4b47-ba5a-0e0ad20afcf3 · outbound

This paper cites Information transparency in business-to- business auction markets: The role of winner identity disclosure.Management Science, 65(9):4261–4279, 2019.

Decoding Consumer Preferences Using Attention-Based Language Models Information transparency in business-to- business auction markets: The role of winner identity disclosure.Management Science, 65(9):4261–4279, 2019

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.313808Z

Source-reported events for the cited work

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

Observation 9c716770-746d-46e8-8b24-344b8e441f4d · outbound

This paper cites Strategic jump bidding in english auctions.

Decoding Consumer Preferences Using Attention-Based Language Models Strategic jump bidding in english auctions

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.303783Z

Source-reported events for the cited work

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

Observation f0c83209-329d-4ad3-9f23-958b6522aea8 · outbound

This paper cites Jump bidding strategies in internet auctions.Management Science, 50(10):1407–1419, 2004.

Decoding Consumer Preferences Using Attention-Based Language Models Jump bidding strategies in internet auctions.Management Science, 50(10):1407–1419, 2004

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.294503Z

Source-reported events for the cited work

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

Observation a620a0e8-2695-4192-a13b-821f1fd23ad7 · outbound

This paper cites Preserving bidder privacy in assignment auctions: design and measurement.

Decoding Consumer Preferences Using Attention-Based Language Models Preserving bidder privacy in assignment auctions: design and measurement

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.285328Z

Source-reported events for the cited work

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

Observation 97ced19d-789d-4d4b-bb87-36a98ae7f259 · outbound

This paper cites Quasi-monte carlo integration.Journal of Computational Physics, 122(2):218–230, 1995.

Decoding Consumer Preferences Using Attention-Based Language Models Quasi-monte carlo integration.Journal of Computational Physics, 122(2):218–230, 1995

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.276081Z

Source-reported events for the cited work

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

Observation 83e43eec-610d-4824-8857-e108d5c18bf6 · outbound

This paper cites John Wiley & Sons, 2002.

Decoding Consumer Preferences Using Attention-Based Language Models John Wiley & Sons, 2002

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.266585Z

Source-reported events for the cited work

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

Observation 7321234b-d1c0-4c99-b34b-05cf698d1cc8 · outbound

This paper cites Axiomatic attribution for deep networks.

Decoding Consumer Preferences Using Attention-Based Language Models Axiomatic attribution for deep networks

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.073004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f52088e-6ef1-4a68-843e-c7b602fb0960 · outbound

This paper cites Research commentary—designing smart markets.

Decoding Consumer Preferences Using Attention-Based Language Models Research commentary—designing smart markets

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.251733Z

Source-reported events for the cited work

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

Observation 53fa0876-b32a-4d23-8dda-130b4925efb4 · outbound

This paper cites Are Transformers universal approximators of sequence-to-sequence functions?.

Decoding Consumer Preferences Using Attention-Based Language Models Are Transformers universal approximators of sequence-to-sequence functions?

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.078612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f63d8c0-0b49-4d8f-a0d7-1744b61ebe79 · outbound

This paper cites A universal approximation theorem of deep neural networks for expressing probability distributions.

Decoding Consumer Preferences Using Attention-Based Language Models A universal approximation theorem of deep neural networks for expressing probability distributions

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.241467Z

Source-reported events for the cited work

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

Observation 98f6d576-2564-4a3f-b324-e703082e14d6 · outbound

This paper cites Multilayer feedforward networks are universal approximators.

Decoding Consumer Preferences Using Attention-Based Language Models Multilayer feedforward networks are universal approximators

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T14:51:53.084470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b049c6a-f721-44e0-8342-5f0921bf1c3a · outbound

This paper cites A reconsideration of hedonic price indexes with an application to pc’s.American Economic Review, 93(5):1578–1596, 2003.

Decoding Consumer Preferences Using Attention-Based Language Models A reconsideration of hedonic price indexes with an application to pc’s.American Economic Review, 93(5):1578–1596, 2003

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T14:51:53.226473Z

Source-reported events for the cited work

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

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