Paper Citation Record · LEDGER

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors

As of 11 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2502.15724.

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

pith.paper-citation-record.v1
2502.15724 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T10:07:33.849583Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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 exact5
verified fuzzy10
unresolved17
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6ee2ec68-8b43-4689-97b5-6a36e7a7c4d3 · outbound

This paper cites A Survey of Large Language Models.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors A Survey of Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.763702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 83912333-f2d9-4d73-b5e4-9055341dc9b5 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors PaLM: Scaling Language Modeling with Pathways

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.768013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53288b3d-354a-46b5-91bd-77643a3ce62d · outbound

This paper cites Holistic Evaluation of Language Models.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Holistic Evaluation of Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.771199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f13e2d9-011e-4e31-b54b-4777bb42d62e · outbound

This paper cites Emergent Abilities of Large Language Models.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Emergent Abilities of Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.773840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d19f771-cb30-42ed-9297-3450b43065ff · outbound

This paper cites ”Forecasting purchase categories by trans- actional data: A comparative study of classification methods.” Lecture Notes in Computer Science.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Forecasting purchase categories by trans- actional data: A comparative study of classification methods.” Lecture Notes in Computer Science

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.241447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1621720a-4601-4b9a-b046-8c3cacada25d · outbound

This paper cites ”Can Generative AI improve social science?” Proceedings of the National Academy of Sciences 121.21 (2024).

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Can Generative AI improve social science?” Proceedings of the National Academy of Sciences 121.21 (2024)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.234585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9dd26c4b-3f1e-4969-b6a8-e34bb75d44a2 · outbound

This paper cites A Survey on Large Language Models for Recommendation.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors A Survey on Large Language Models for Recommendation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.781711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d335a7e-d778-43b1-a30d-7e02d9c8a367 · outbound

This paper cites Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5).

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.784343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac4e84f0-bff5-4b9a-9443-9ed1c029558a · outbound

This paper cites an unresolved cited work.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-10T10:07:34.227548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 57db917b-4452-42bb-8180-4d61d4d988ac · outbound

This paper cites Zero-Shot Recommendation as Language Modeling.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Zero-Shot Recommendation as Language Modeling

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:07:34.119839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0db333af-8d2d-4e91-8284-6373778e7b4e · outbound

This paper cites Zero-Shot Recommender Systems.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Zero-Shot Recommender Systems

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.790963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f63032b1-2210-4182-bbb7-c981274a282e · outbound

This paper cites PALR: Personalization Aware LLMs for Recommendation.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors PALR: Personalization Aware LLMs for Recommendation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.793914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7024e2f-4f1a-4dbb-a10f-3e4c53586be7 · outbound

This paper cites Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Learning Vector-Quantized Item Representation for Transferable Sequential Recommenders

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:07:34.096775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6cd0e9d5-048a-42b6-9327-0feb3288e014 · outbound

This paper cites ”Instruction Tuning for Large Language Models: A Survey.” arXiv preprint arXiv:2308.10792 (2024).

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Instruction Tuning for Large Language Models: A Survey.” arXiv preprint arXiv:2308.10792 (2024)

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.799626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c242670-20b1-45ee-bf32-b634cd91abe6 · outbound

This paper cites A Survey on Data Selection for LLM Instruction Tuning.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors A Survey on Data Selection for LLM Instruction Tuning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.802787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfd923ae-29df-4c74-9e75-b460e7106bea · outbound

This paper cites Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:07:33.926962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ad3d5349-a31f-41c0-86d0-e3ea1e08e439 · outbound

This paper cites ”Learning and adaptivity in interac- tive recommender systems.” ICEC ’07: Proceedings of the Ninth International Conference on Electronic Commerce (2007): 75-84.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Learning and adaptivity in interac- tive recommender systems.” ICEC ’07: Proceedings of the Ninth International Conference on Electronic Commerce (2007): 75-84

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.219615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 14d9495f-565c-46d2-9196-5b5170ce0799 · outbound

This paper cites ”Factoriz- ing personalized Markov chains for next-basket recommendation.” WWW ’10: 16 Proceedings of the 19th International Conference on World Wide Web (2010): 811-820.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Factoriz- ing personalized Markov chains for next-basket recommendation.” WWW ’10: 16 Proceedings of the 19th International Conference on World Wide Web (2010): 811-820

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.211734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab6c2104-cb76-41e9-9a9b-3ff6f724376a · outbound

This paper cites Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:07:33.917533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c78a223-be16-4a75-9b59-3bb0c6403a7f · outbound

This paper cites ”Sequential Recommender Systems: Challenges, Progress and Prospects.” IJCAI-2019: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (2019).

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Sequential Recommender Systems: Challenges, Progress and Prospects.” IJCAI-2019: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (2019)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.203813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e6a2b69a-67b3-4904-be63-4946645c3eb8 · outbound

This paper cites Session-based Recommendations with Recurrent Neural Networks.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Session-based Recommendations with Recurrent Neural Networks

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.819842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3172f3d3-a7bd-4965-bf6c-bd9354b0d7df · outbound

This paper cites ”Sequential User-based Recur- rent Neural Network Recommendations.” RecSys ’17: Proceedings of the Eleventh ACM Conference on Recommender Systems (2017): 152-160.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Sequential User-based Recur- rent Neural Network Recommendations.” RecSys ’17: Proceedings of the Eleventh ACM Conference on Recommender Systems (2017): 152-160

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.196289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bcc2fafe-a499-4ff9-843c-9156b651f51f · outbound

This paper cites A Simple Convolutional Generative Network for Next Item Recommendation.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors A Simple Convolutional Generative Network for Next Item Recommendation

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T10:07:33.901736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d71aafd7-f83a-4574-bd17-061f8dee2d2e · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors LoRA: Low-Rank Adaptation of Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.829530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0797d491-f17c-48e1-81ef-710af8334bae · outbound

This paper cites ”PEFT: State-of-the-art Parameter-Efficient Fine- Tuning methods.” GitHub repository.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”PEFT: State-of-the-art Parameter-Efficient Fine- Tuning methods.” GitHub repository

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.188557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 88c2bfbf-57c5-497a-9b57-33a418fd4f78 · outbound

This paper cites ”TRL: Transformer Reinforcement Learning.” GitHub repository.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”TRL: Transformer Reinforcement Learning.” GitHub repository

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.181337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fbef7ea0-10a7-4b3d-96f7-8913d4307772 · outbound

This paper cites Mistral 7B.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Mistral 7B

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.836578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b112c21-9f44-4036-82ae-fe08ea2f2525 · outbound

This paper cites ”Behavioral attributes and financial churn prediction.” EPJ Data Science 7.1 (2018): 1-18.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Behavioral attributes and financial churn prediction.” EPJ Data Science 7.1 (2018): 1-18

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.174662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a8135661-1e9b-4f62-b68a-c91424ba2097 · outbound

This paper cites ”Money Walks: Implicit Mobility Behavior and Financial Well-Being.” PLOS ONE 10.8 (2015): e0136628.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Money Walks: Implicit Mobility Behavior and Financial Well-Being.” PLOS ONE 10.8 (2015): e0136628

Reference 29

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T10:07:34.167609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6901ecaa-de2b-41ca-bfb5-66af05fc5d1c · outbound

This paper cites Training language models to follow instructions with human feedback.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Training language models to follow instructions with human feedback

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.842331Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46351c17-ecfd-416c-9aee-b83adc8e0df6 · outbound

This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.844237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e5c2383-539d-4fa1-b1a3-9d167b6e7dc9 · outbound

This paper cites ”Stanford Alpaca: An Instruction-following LLaMA model.” GitHub repository.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors ”Stanford Alpaca: An Instruction-following LLaMA model.” GitHub repository

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T10:07:34.161075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 141c8acd-f8f8-4964-aaa7-8bad77c96dfc · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Instruction-Based Fine-tuning of Open-Source LLMs for Predicting Customer Purchase Behaviors LLaMA: Open and Efficient Foundation Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T10:07:33.849583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.