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題名 由交易紀錄建立客戶輪廓以進行大型語言模型之少樣本商品推薦
Leveraging User Personas from Transactions for Few-shot Item Recommendation with Large Language Models
作者 張祐誠
Chang, Yu-Cheng
貢獻者 沈錳坤
Shan, Man-Kwan
張祐誠
Chang, Yu-Cheng
關鍵詞 大型語言模型
客戶輪廓
推薦系統
直接推薦
少樣本學習
Large Language Models
Customer Persona
Recommendation System
Direct Recommendation
Few-shot Learning
日期 2025
上傳時間 1-Sep-2025 16:19:48 (UTC+8)
參考文獻 [1] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al., Language Models are Few-Shot Learners, Advances in Neural Information Processing Systems, 2020 [2] Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, et al., A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys), In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024 [3] Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, et al., Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation, In Proceedings of the 37th International Conference on Neural Information Processing Systems, 2023 [4] Jinhyuk Lee, Feiyang Chen, Sahil Dua, Daniel Cer, Madhuri Shanbhogue, et al., Gemini Embedding: Generalizable Embeddings from Gemini, CoRR, 2025 [5] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, et al., How Can Recommender Systems Benefit from Large Language Models: A Survey, ACM Transactions on Information Systems, Vol. 43, No. 2, 2025 [6] Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta, Taibai Xu, et al., STAR: A Simple Training-free Approach for Recommendations using Large Language Models, CoRR, 2024 [7] Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, et al., LLMRec: Benchmarking Large Language Models on Recommendation Task, CoRR, 2023 [8] Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, et al., Is ChatGPT a Good Recommender? A Preliminary Study, In Proceedings of the 1st Workshop on Recommendation with Generative Models, co-located with the 32nd ACM International Conference on Information and Knowledge Management, 2023 [9] Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca, Terry Koo, Lucas Dixon, et al., We Need Structured Output: Towards User-centered Constraints on Large Language Model Output, Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024 [10] Sebastian Lubos, Thi Ngoc Trang Tran, Alexander Felfernig, Seda Polat Erdeniz, and Viet-Man Le, LLM-generated Explanations for Recommender Systems, Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 2024 [11] Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, et al., Taxonomy-Guided Zero-Shot Recommendations with LLMs, In Proceedings of the 31st International Conference on Computational Linguistics, 2025 [12] Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, et al., A Comprehensive Overview of Large Language Models, ACM Transactions on Intelligent Systems and Technology, 2025 [13] Wenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin, et al., Generative Next-Basket Recommendation, In Proceedings of the 17th ACM Conference on Recommender Systems, 2023 [14] Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen, Large Language Models for Generative Recommendation: A Survey and Visionary Discussions, Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, 2024 [15] Joni Salminen, Chang Liu, Wenjing Pian, Jianxing Chi, Essi Häyhänen, et al., Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona Descriptions, In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 2024 [16] Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, An Empirical Study of Next-basket Recommendations, CoRR, 2023 [17] Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, et al., Rethinking Large Language Model Architectures for Sequential Recommendations, CoRR, 2024 [18] Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, et al., A Survey on Large Language Models for Recommendation, World Wide Web, Vol. 27, No. 5, 2024 [19] Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang, OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems, In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024 [20] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, et al., PALR: Personalization Aware LLMs for Recommendation, arXiv:2305.07622, 2023 [21] Joyce Zhou, Yijia Dai, and Thorsten Joachims, Language-Based User Profiles for Recommendation, CoRR, 2024
描述 碩士
國立政治大學
資訊科學系碩士在職專班
112971013
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0112971013
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man-Kwanen_US
dc.contributor.author (Authors) 張祐誠zh_TW
dc.contributor.author (Authors) Chang, Yu-Chengen_US
dc.creator (作者) 張祐誠zh_TW
dc.creator (作者) Chang, Yu-Chengen_US
dc.date (日期) 2025en_US
dc.date.accessioned 1-Sep-2025 16:19:48 (UTC+8)-
dc.date.available 1-Sep-2025 16:19:48 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2025 16:19:48 (UTC+8)-
dc.identifier (Other Identifiers) G0112971013en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/159298-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 112971013zh_TW
dc.description.tableofcontents 第一章 緒論 1 第1.1節 研究背景 1 第1.2節 研究動機 2 第1.3節 研究目的 3 第1.4節 研究貢獻 3 第二章 文獻探討 4 第2.1節 大型語言模型Non-tuning做法與直接作為推薦引擎 4 第2.2節 大型語言模型應用於Top N直接推薦任務 5 第2.3節 大型語言模型用於客戶輪廓生成 5 第三章 研究方法 6 第3.1節 Persona Generation 模組 6 第3.2節 Persona Similarity Retrieval模組 10 第3.3節 Prompt Construction 模組 11 第3.4節 Recommendation 模組 14 第四章 實驗 15 第4.1節 研究資料 15 第4.2節 評估指標 18 第4.3節 實驗設計 19 第4.4節 實驗結果 20 第4.4.1節 Item Prediction實驗結果 21 第4.4.2節 相似 User 人數 K 對於 LLM 直接推薦影響 21 第4.4.3節 LLM 預測數量分佈分析 22 第4.4.4節 比較不同商品推薦數量N對於準確度的變化 23 第4.4.5節 中英文客戶輪廓推理實驗結果比較 24 第4.4.6節 Persona 生成內容分析 26 第4.4.7節 Persona 的生成內容與預測準確率分析 31 第4.5節 實驗總結 33 第五章 結論與未來研究 35 參考文獻 37zh_TW
dc.format.extent 1832319 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0112971013en_US
dc.subject (關鍵詞) 大型語言模型zh_TW
dc.subject (關鍵詞) 客戶輪廓zh_TW
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 直接推薦zh_TW
dc.subject (關鍵詞) 少樣本學習zh_TW
dc.subject (關鍵詞) Large Language Modelsen_US
dc.subject (關鍵詞) Customer Personaen_US
dc.subject (關鍵詞) Recommendation Systemen_US
dc.subject (關鍵詞) Direct Recommendationen_US
dc.subject (關鍵詞) Few-shot Learningen_US
dc.title (題名) 由交易紀錄建立客戶輪廓以進行大型語言模型之少樣本商品推薦zh_TW
dc.title (題名) Leveraging User Personas from Transactions for Few-shot Item Recommendation with Large Language Modelsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, et al., Language Models are Few-Shot Learners, Advances in Neural Information Processing Systems, 2020 [2] Yashar Deldjoo, Zhankui He, Julian J. McAuley, Anton Korikov, Scott Sanner, et al., A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys), In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024 [3] Wei Jin, Haitao Mao, Zheng Li, Haoming Jiang, Chen Luo, et al., Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation, In Proceedings of the 37th International Conference on Neural Information Processing Systems, 2023 [4] Jinhyuk Lee, Feiyang Chen, Sahil Dua, Daniel Cer, Madhuri Shanbhogue, et al., Gemini Embedding: Generalizable Embeddings from Gemini, CoRR, 2025 [5] Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, et al., How Can Recommender Systems Benefit from Large Language Models: A Survey, ACM Transactions on Information Systems, Vol. 43, No. 2, 2025 [6] Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta, Taibai Xu, et al., STAR: A Simple Training-free Approach for Recommendations using Large Language Models, CoRR, 2024 [7] Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, et al., LLMRec: Benchmarking Large Language Models on Recommendation Task, CoRR, 2023 [8] Junling Liu, Chao Liu, Peilin Zhou, Renjie Lv, Kang Zhou, et al., Is ChatGPT a Good Recommender? A Preliminary Study, In Proceedings of the 1st Workshop on Recommendation with Generative Models, co-located with the 32nd ACM International Conference on Information and Knowledge Management, 2023 [9] Michael Xieyang Liu, Frederick Liu, Alexander J. Fiannaca, Terry Koo, Lucas Dixon, et al., We Need Structured Output: Towards User-centered Constraints on Large Language Model Output, Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024 [10] Sebastian Lubos, Thi Ngoc Trang Tran, Alexander Felfernig, Seda Polat Erdeniz, and Viet-Man Le, LLM-generated Explanations for Recommender Systems, Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, 2024 [11] Yueqing Liang, Liangwei Yang, Chen Wang, Xiongxiao Xu, Philip S. Yu, et al., Taxonomy-Guided Zero-Shot Recommendations with LLMs, In Proceedings of the 31st International Conference on Computational Linguistics, 2025 [12] Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, et al., A Comprehensive Overview of Large Language Models, ACM Transactions on Intelligent Systems and Technology, 2025 [13] Wenqi Sun, Ruobing Xie, Junjie Zhang, Wayne Xin Zhao, Leyu Lin, et al., Generative Next-Basket Recommendation, In Proceedings of the 17th ACM Conference on Recommender Systems, 2023 [14] Lei Li, Yongfeng Zhang, Dugang Liu, and Li Chen, Large Language Models for Generative Recommendation: A Survey and Visionary Discussions, Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, 2024 [15] Joni Salminen, Chang Liu, Wenjing Pian, Jianxing Chi, Essi Häyhänen, et al., Deus Ex Machina and Personas from Large Language Models: Investigating the Composition of AI-Generated Persona Descriptions, In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 2024 [16] Zhufeng Shao, Shoujin Wang, Qian Zhang, Wenpeng Lu, Zhao Li, An Empirical Study of Next-basket Recommendations, CoRR, 2023 [17] Hanbing Wang, Xiaorui Liu, Wenqi Fan, Xiangyu Zhao, Venkataramana Kini, et al., Rethinking Large Language Model Architectures for Sequential Recommendations, CoRR, 2024 [18] Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, et al., A Survey on Large Language Models for Recommendation, World Wide Web, Vol. 27, No. 5, 2024 [19] Shuyuan Xu, Wenyue Hua, and Yongfeng Zhang, OpenP5: An Open-Source Platform for Developing, Training, and Evaluating LLM-based Recommender Systems, In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024 [20] Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, et al., PALR: Personalization Aware LLMs for Recommendation, arXiv:2305.07622, 2023 [21] Joyce Zhou, Yijia Dai, and Thorsten Joachims, Language-Based User Profiles for Recommendation, CoRR, 2024zh_TW