Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs | Paper Citation Record

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This paper cites Algorithmic fairness datasets: the story so far,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Algorithmic fairness datasets: the story so far,

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This paper cites Social data: Biases, method- ological pitfalls, and ethical boundaries,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Social data: Biases, method- ological pitfalls, and ethical boundaries,

Reference 2

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This paper cites Software Engineering for Fairness: A Case Study with Hyperparameter Optimization.

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This paper cites Fairness-aware machine learning engineering: how far are we?.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Fairness-aware machine learning engineering: how far are we?

Reference 4

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This paper cites [Online].

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This paper cites Development of recommendation systems for software engineering: the CROSSMINER experience,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Development of recommendation systems for software engineering: the CROSSMINER experience,

Reference 7

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This paper cites Diversified third-party library prediction for mobile app development,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Diversified third-party library prediction for mobile app development,

Reference 9

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This paper cites Libd: Scalable and precise third- party library detection in android markets,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Libd: Scalable and precise third- party library detection in android markets,

Reference 10

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This paper cites CrossRec: Supporting Software Developers by Recommending Third-party Libraries,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs CrossRec: Supporting Software Developers by Recommending Third-party Libraries,

Reference 11

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This paper cites Improving reusability of software libraries through usage pattern mining,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Improving reusability of software libraries through usage pattern mining,

Reference 12

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This paper cites Automated library recommendation,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Automated library recommendation,

Reference 13

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This paper cites Managing popularity bias in recommender systems with personalized re-ranking,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Managing popularity bias in recommender systems with personalized re-ranking,

Reference 14

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This paper cites The unfairness of popularity bias in recommendation,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs The unfairness of popularity bias in recommendation,

Reference 15

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This paper cites Bias and debias in recommender system: A survey and future directions,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Bias and debias in recommender system: A survey and future directions,

Reference 16

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This paper cites Fairness in recommender systems: Research landscape and future directions,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Fairness in recommender systems: Research landscape and future directions,

Reference 17

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This paper cites A survey on popularity bias in recommender systems,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs A survey on popularity bias in recommender systems,

Reference 18

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This paper cites Dealing with popularity bias in recommender systems for third-party libraries: How far are we?.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Dealing with popularity bias in recommender systems for third-party libraries: How far are we?

Reference 19

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This paper cites Anderson, The Long Tail: Why the Future of Business Is Selling Less of More.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Anderson, The Long Tail: Why the Future of Business Is Selling Less of More

Reference 20

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This paper cites Evaluating recommender systems,.

Reference 21

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This paper cites Improving sales diversity by recommending users to items,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Improving sales diversity by recommending users to items,

Reference 22

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This paper cites Large language models for software engineering: A systematic literature review,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Large language models for software engineering: A systematic literature review,

Reference 23

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This paper cites An empirical study on the usage of transformer models for code completion,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs An empirical study on the usage of transformer models for code completion,

Reference 24

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This paper cites Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Studying the Usage of Text-To-Text Transfer Transformer to Support Code-Related Tasks,

Reference 25

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This paper cites The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models ,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs The Devil is in the Tails: How Long-Tailed Code Distributions Impact Large Language Models ,

Reference 26

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This paper cites Empirical study of transformers for source code,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Empirical study of transformers for source code,

Reference 27

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This paper cites Retrieval-augmented generation for knowledge-intensive nlp tasks,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Retrieval-augmented generation for knowledge-intensive nlp tasks,

Reference 29

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This paper cites Endowing third-party libraries recommender systems with explicit user feedback mechanisms,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Endowing third-party libraries recommender systems with explicit user feedback mechanisms,

Reference 30

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This paper cites A survey on bias and fairness in machine learning,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs A survey on bias and fairness in machine learning,

Reference 31

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This paper cites Beyond accuracy: Evaluating recommender systems by coverage and serendipity,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Beyond accuracy: Evaluating recommender systems by coverage and serendipity,

Reference 32

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This paper cites Too long; didn’t read: Automatic summarization of github readme. md with transformers,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Too long; didn’t read: Automatic summarization of github readme. md with transformers,

Reference 33

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Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Process Modeling with Large Language Models,

Reference 34

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Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Language models are unsupervised multitask learners,

Reference 35

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Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs LoRA: Low-rank adaptation of large language models,

Reference 37

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This paper cites EqBal-RS: Mitigating popularity bias in recommender systems,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs EqBal-RS: Mitigating popularity bias in recommender systems,

Reference 38

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This paper cites Debiaser for multiple variables to enhance fairness in classification tasks,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Debiaser for multiple variables to enhance fairness in classification tasks,

Reference 39

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This paper cites Automatic fairness testing of machine learning models,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Automatic fairness testing of machine learning models,

Reference 40

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This paper cites Fairway: a way to build fair ML software,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Fairway: a way to build fair ML software,

Reference 41

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This paper cites Beyond words: On large language models actionability in mission-critical risk analysis,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Beyond words: On large language models actionability in mission-critical risk analysis,

Reference 42

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This paper cites BRAID: an API recommender supporting implicit user feedback,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs BRAID: an API recommender supporting implicit user feedback,

Reference 43

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Reference 44

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This paper cites Avalanche: an End-to-End Library for Continual Learning.

Reference 45

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This paper cites Req2lib: A semantic neural model for software library recommendation,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Req2lib: A semantic neural model for software library recommendation,

Reference 46

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This paper cites Embedding app-library graph for neural third party library recommendation,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Embedding app-library graph for neural third party library recommendation,

Reference 47

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This paper cites Mining likely analogical apis across third-party libraries via large-scale unsupervised api semantics embed- ding,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Mining likely analogical apis across third-party libraries via large-scale unsupervised api semantics embed- ding,

Reference 48

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This paper cites What’s spain’s paris? mining analogical libraries from q&a discussions,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs What’s spain’s paris? mining analogical libraries from q&a discussions,

Reference 49

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This paper cites How scale affects structure in java programs,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs How scale affects structure in java programs,

Reference 50

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This paper cites Understanding the factors that impact the popularity of github repositories,.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Understanding the factors that impact the popularity of github repositories,

Reference 51

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This paper cites Available: https://doi-org.univaq.idm.oclc.org/10.1145/ 2858965.2814300.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Available: https://doi-org.univaq.idm.oclc.org/10.1145/ 2858965.2814300

Reference 2015

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This paper cites 2020, pp.

Reference 2020

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This paper cites Available: https://doi.org/10.1007/s11257-023-09364-z.

Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs Available: https://doi.org/10.1007/s11257-023-09364-z

Reference 2022

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