Otimização de Prompts para Explicações em Sistemas de Recomendação: Balanceando Diversidade e Popularidade

• Gabriel Prenassi UFSJ
• André Levi Zanon Centro Universitário FEI
• Henrique Sekido USP
• Yan Andrade UFMG
• Marcelo G. Manzato Centro Universitário FEI
• Leonardo Rocha UFSJ

DOI: https://doi.org/10.5753/webmedia.2026.30236

Resumo

Recommender systems are widely used to provide personalized item suggestions, however the increasing complexity of recommendation algorithms has intensified the lack of transparency about why recommendations are generated. Selecting effective explanations remains challenging, especially when selecting attributes (i.e. shared information of different items) that are both diverse and recognizable to users. Popular attributes tend to be more familiar, while diverse attributes help avoid repetitive explanations, creating a trade-off between these two dimensions. This paper investigates the use of prompt optimization for Large Language Models to select explanations in recommender systems that balance attribute diversity and popularity. We use a knowledge graph in the movie domain, connecting items to their attributes, and generate recommendations for users. Then, we apply a prompt optimization strategy to guide the LLM toward explanations that balance the popularity and diversity of attributes. Experiments using literature-based metrics show that optimized prompts can improve the joint balance between these two metrics, outperforming non-optimized prompts and a comparable non-LLM explanation method.

Palavras-chave: Recommender Systems, Explainability, Large Language Models, Prompt Optimization, Knowledge Graphs, Attribute Diversity, Attribute Popularity

Referências

Charu C. Aggarwal. 2016. Recommender Systems. Springer US. DOI: 10.1007/978-3-319-29659-3

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019. Optuna: A Next-generation Hyperparameter Optimization Framework. arXiv:1907.10902 [cs.LG] [link]

Giacomo Balloccu, Ludovico Boratto, Gianni Fenu, and Mirko Marras. 2022. Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22). Association for Computing Machinery, New York, NY, USA, 646–656. DOI: 10.1145/3477495.3532041

Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, Dzmitry Bahdanau, Nicolas Chapados, and Siva Reddy. 2024. LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. In First Conference on Language Modeling. [link]

Diego Carraro and Derek Bridge. 2025. Enhancing Recommendation Diversity by Re-ranking with Large Language Models. ACM Trans. Recomm. Syst. 4, 2, Article 18 (Nov. 2025), 40 pages. DOI: 10.1145/3700604

Jin Chen, Zheng Liu, Xu Huang, Chenwang Wu, Qi Liu, Gangwei Jiang, Yuanhao Pu, Yuxuan Lei, Xiaolong Chen, Xingmei Wang, Kai Zheng, Defu Lian, and Enhong Chen. 2024. When large language models meet personalization: perspectives of challenges and opportunities. World Wide Web 27, 4 (June 2024), 45 pages. DOI: 10.1007/s11280-024-01276-1

Yumin Choi, Jinheon Baek, and Sung Ju Hwang. 2025. System Prompt Optimization with Meta-Learning. In Advances in Neural Information Processing Systems, D. Belgrave, C. Zhang, H. Lin, R. Pascanu, P. Koniusz, M. Ghassemi, and N. Chen (Eds.), Vol. 38. Curran Associates, Inc., 55451–55488. [link]

Arthur da Costa, Eduardo Fressato, Fernando Neto, Marcelo Manzato, and Ricardo Campello. 2018. Case recommender: a flexible and extensible python framework for recommender systems. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 494–495. DOI: 10.1145/3240323.3241611

Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021. A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39, 2, Article 20 (Jan. 2021), 49 pages. DOI: 10.1145/3434185

Jade Goldstein and Jaime Carbonell. 1998. Summarization: (1) using MMR for diversity - based reranking and (2) evaluating summaries. In Proceedings of a Workshop on Held at Baltimore, Maryland: October 13-15, 1998 (Baltimore, Maryland) (TIPSTER ’98). Association for Computational Linguistics, USA, 181–195. DOI: 10.3115/1119089.1119120

Scott Graham, Jun-Ki Min, and Tao Wu. 2019. Microsoft recommenders: tools to accelerate developing recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 542–543. DOI: 10.1145/3298689.3346967

Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi. 2018. A Survey of Methods for Explaining Black Box Models. ACM Comput. Surv. 51, 5, Article 93 (Aug. 2018), 42 pages. DOI: 10.1145/3236009

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst. 5, 4, Article 19 (Dec. 2015), 19 pages. DOI: 10.1145/2827872

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In Proceedings of the 26th International Conference on World Wide Web (Perth, Australia) (WWW ’17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173–182. DOI: 10.1145/3038912.3052569

Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich. 2010. Recommender systems: an introduction. Cambridge university press.

Yuxuan Lei, Jianxun Lian, Jing Yao, Xu Huang, Defu Lian, and Xing Xie. 2024. RecExplainer: Aligning Large Language Models for Explaining Recommendation Models. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Barcelona, Spain) (KDD ’24). Association for Computing Machinery, New York, NY, USA, 1530–1541. DOI: 10.1145/3637528.3671802

Cataldo Musto, Fedelucio Narducci, Pasquale Lops, Marco De Gemmis, and Giovanni Semeraro. 2016. ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 151–154. DOI: 10.1145/2959100.2959173

Ingrid Nunes and Dietmar Jannach. 2017. A systematic review and taxonomy of explanations in decision support and recommender systems. User Modeling and User-Adapted Interaction 27, 3-5 (2017), 393–444. DOI: 10.1007/s11257-017-9195-0

Yicui Peng, Hao Chen, Ching-Sheng Lin, Guo Huang, Jinrong Hu, Hui Guo, Bin Kong, Shu Hu, Xi Wu, and Xin Wang. 2024. Uncertainty-Aware Explainable Recommendation with Large Language Models. In 2024 International Joint Conference on Neural Networks (IJCNN). 1–8. DOI: 10.1109/IJCNN60899.2024.10651104

Gabriel Prenassi, Rodrigo Souza, Henrique Sekido, Guilherme Fonseca, Marcelo Manzato, and Leonardo Rocha. 2025. Calibração de Sistemas de Recomendação com LLMs: Otimização de Prompts para Balancear Precisão, Diversidade e Justiça. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/RJ). SBC, Porto Alegre, RS, Brasil, 103–111. DOI: 10.5753/webmedia.2025.15613

Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.). Association for Computational Linguistics, Hong Kong, China, 3982–3992. DOI: 10.18653/v1/D19-1410

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI ’09). AUAI Press, Arlington, Virginia, USA, 452–461.

Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work (Chapel Hill, North Carolina, USA) (CSCW ’94). Association for Computing Machinery, New York, NY, USA, 175–186. DOI: 10.1145/192844.192905

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to Recommender Systems Handbook. Springer US, Boston, MA, 1–35. DOI: 10.1007/978-0-387-85820-3_1

Gustavo Santos, Myriam Delgado, Daniel Silver, and Thiago Silva. 2025. Where Next? A Behavioral and Explainable Framework for City and Neighborhood Recommendation. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/RJ). SBC, Porto Alegre, RS, Brasil, 482–490. DOI: 10.5753/webmedia.2025.15492

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (Hong Kong, Hong Kong) (WWW ’01). Association for Computing Machinery, New York, NY, USA, 285–295. DOI: 10.1145/371920.372071

Nava Tintarev and Judith Masthoff. 2015. Explaining recommendations: Design and evaluation. In Recommender systems handbook. Springer, 353–382.

Lequn Wang, Jiangwei Pan, Fengdi Che, and Linas Baltrunas. 2026. GenPage: Towards End-to-End Generative Homepage Construction at Netflix. arXiv preprint arXiv:2606.31031 (2026).

Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric Xing, and Zhiting Hu. 2024. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization. In International Conference on Learning Representations, B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (Eds.), Vol. 2024. 23967–24001. [link]

Frank Wilcoxon. 1945. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1, 6 (1945), 80–83. [link]

Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2024. Large Language Models as Optimizers. In International Conference on Learning Representations, B. Kim, Y. Yue, S. Chaudhuri, K. Fragkiadaki, M. Khan, and Y. Sun (Eds.), Vol. 2024. 12028–12068. [link]

Dayu Yang, Fumian Chen, and Hui Fang. 2024. Behavior Alignment: A New Perspective of Evaluating LLM-based Conversational Recommendation Systems. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24). Association for Computing Machinery, New York, NY, USA, 2286–2290. DOI: 10.1145/3626772.3657924

André Zanon, Leonardo Rocha, and Marcelo Manzato. 2024. O Impacto de Estratégias de Embeddings de Grafos na Explicabilidade de Sistemas de Recomendação. In Proceedings of the 30th Brazilian Symposium on Multimedia and the Web (Juiz de Fora/MG). SBC, Porto Alegre, RS, Brasil, 231–239. DOI: 10.5753/webmedia.2024.241857

André Levi Zanon, Leonardo Chaves Dutra da Rocha, and Marcelo Garcia Manzato. 2024. Model-agnostic knowledge graph embedding explanations for recommender systems. In World Conference on Explainable Artificial Intelligence. Springer, 3–27.

André Levi Zanon, Leonardo Chaves Dutra da Rocha, and Marcelo Garcia Manzato. 2025. Multi-Objective Optimization of Explanation Metrics in Recommender Systems with LLMs. In 2025 IEEE 37th International Conference on Tools with Artificial Intelligence (ICTAI). 149–156. DOI: 10.1109/ICTAI66417.2025.00027

Yongfeng Zhang and Xu Chen. 2020. Explainable Recommendation: A Survey and New Perspectives. Foundations and Trends® in Information Retrieval 14, 1 (2020), 1–101. DOI: 10.1561/1500000066

Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023. Large Language Models are Human-Level Prompt Engineers. In The Eleventh International Conference on Learning Representations. [link]

Kaijie Zhu, Jindong Wang, Jiaheng Zhou, Zichen Wang, Hao Chen, Yidong Wang, Linyi Yang, Wei Ye, Yue Zhang, Neil Gong, and Xing Xie. 2024. PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts. In Proceedings of the 1st ACM Workshop on Large AI Systems and Models with Privacy and Safety Analysis (Salt Lake City, UT, USA) (LAMPS ’24). Association for Computing Machinery, New York, NY, USA, 57–68. DOI: 10.1145/3689217.3690621403