Tudo é Ranking! Adaptações Cruzadas entre Busca e Recomendação
Resumo
Information Retrieval (IR) and Recommender Systems (RS) have historically evolved as distinct research areas, with different data representations, algorithms, and evaluation practices. Yet, both are fundamentally concerned with ranking items according to user needs, preferences, or intent. This paper explores this shared foundation by investigating whether native algorithms from one paradigm can operate in the other through representation-level adaptations, without modifying their internal architectures. We propose two cross-task strategies: Tag-Query, which converts user interaction histories into textual queries for recommendation using retrieval models, and Query-as-User, which converts search queries into pseudouser profiles for retrieval using recommender models. We evaluate these strategies using a robust experimental framework that encompasses multiple datasets, strong native baselines from both IR and RS, and complementary ranking metrics. The results show that these simple adaptations can achieve competitive performance relative to specialized models and, under certain conditions, outperform native baselines on specific evaluation metrics. More broadly, our findings suggest that meaningful transfer between search and recommendation can emerge through representation-level adaptations, indicating that the two paradigms may be more interoperable than traditionally assumed and opening a research path toward lightweight, reusable, and hybrid ranking pipelines.
Palavras-chave:
information retrieval, recommender systems, ranking, crosstask adaptation, hybrid systems
Referências
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Murilo Cunha, Marilia Silveira, Brenda Santana, Larissa Freitas, and Ulisses Corrêa. 2025. Optimizing and Evaluating a Retrieval-Augmented Generation System for Normative Document Retrieval in Hospital Settings. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/ RJ). SBC, Porto Alegre, RS, Brasil, 385–393. DOI: 10.5753/webmedia.2025.16029
Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An Effective, Efficient, and Scalable Confidence-Based Instance Selection Framework for Transformer-Based Text Classification. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 665–674. DOI: 10.1145/3539618.3591638
Washington Cunha, Felipe Viegas, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2025. A Noise-Oriented and Redundancy-Aware Instance Selection Framework. ACM Transactions on Information Systems 43, 1 (2025). DOI: 10.1145/3701769
David Fernandes, Edleno Silva de Moura, Berthier A. Ribeiro-Neto, Altigran Soares da Silva, and Marcos André Gonçalves. 2007. Computing Block Importance for Searching on Web Sites. In Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management. ACM, 165–174. DOI: 10.1145/1321440.1321466
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F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015).
Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, and Julian J. McAuley. 2025. Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, WSDM 2025. 866–875. DOI: 10.1145/3701551.3703573
K. Järvelin and J. Kekäläinen. 2002. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (October 2002), 422–446.
Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 39–48. DOI: 10.1145/3397271.3401075
Maciej Kula. 2015. Metadata Embeddings for User and Item Coldstart Recommendations. In Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems (CBRecSys 2015) (CEUR Workshop Proceedings, Vol. 1448). 14–21.
Yongqi Li, Xinyu Lin,WenjieWang, Fuli Feng, Liang Pang,Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024. A Survey of Generative Search and Recommendation in the Era of Large Language Models. ArXiv abs/2404.16924 (2024). [link]
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations. CoRR abs/2102.10073 (2021). arXiv:2102.10073 [link]
Vinicius Machado, Murilo Schmitt, and Eduardo Spinosa. 2025. Extrapolation-Based Data Augmentation for Sequential Recommendation. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/RJ). SBC, Porto Alegre, RS, Brasil, 238–247. DOI: 10.5753/webmedia.2025.15127
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press.
S. Merlo, G. Faggioli, and N. Ferro. 2025. Joint Information Retrieval and Recommendation: a Reproducibility Study. In Proc. 33rd Italian Symposium on Advanced Database Systems (SEBD 2025), I. Bartolini, G. Cima, D. Firmani, D. Lembo, D. Martinenghi, M. Mecella, and A. Poggi (Eds.). CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, [link], 165–175.
Marc Najork. 2023. Generative Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 1. DOI: 10.1145/3539618.3591871
Michael J. Pazzani and Daniel Billsus. 2007. Content-Based Recommendation Systems. In The Adaptive Web. Springer, 325–341.
Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco de Nadai, and Hugues Bouchard. 2024. Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?. In Proceedings of the 18th ACM Conference on Recommender Systems. DOI: 10.1145/3640457.3688123
Stephen Robertson and Hugo Zaragoza. 2009. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval 3, 4 (2009), 333–389. DOI: 10.1561/1500000019
Weichen Shen. 2017. DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. [link].
Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, and Enyun Yu. 2025. Unified Generative Search and Recommendation. arXiv preprint arXiv:2504.05730 (2025).
Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang Song, Kun Gai, and Ji-Rong Wen. 2023. When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 1313–1323. DOI: 10.1145/3539618.3591786
Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano CM Pereira, and Leonardo Rocha. 2022. Irec: An interactive recommendation framework. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3165–3175.
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024. IDGenRec: LLM-RecSys Alignment with Textual ID Learning. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 355–364. DOI: 10.1145/3626772.3657821
Ash Vardanian. 2023. USearch by Unum Cloud. DOI: 10.5281/zenodo.7949416
Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, and Marcos Goncalves. 2020. CluHTM: Semantic Hierarchical Topic Modeling Based on CluWords. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 8138–8150. DOI: 10.18653/v1/2020.acl-main.724
Jesse Vig, Shilad Sen, and John Riedl. 2012. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction. ACM Trans. Interact. Intell. Syst. 2, 3, Article 13 (Sept. 2012), 44 pages. DOI: 10.1145/2362394.2362395
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and FuruWei. 2024. Multilingual E5 Text Embeddings: A Technical Report. arXiv preprint arXiv:2402.05672 (2024).
Qianyi Wang. 2025. Comparison of Different Recommendation Models based on Amazon Platform Dataset. In Proceedings of the 2024 5th International Conference on Computer Science and Management Technology (ICCSMT ’24). Association for Computing Machinery, New York, NY, USA, 74–79. DOI: 10.1145/3708036.3708048
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024. LLMRec: Large Language Models with Graph Augmentation for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024, Luz Angelica Caudillo-Mata, Silvio Lattanzi, Andrés Muñoz Medina, Leman Akoglu, Aristides Gionis, and Sergei Vassilvitskii (Eds.). ACM, 806–815. DOI: 10.1145/3616855.3635853
Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, and Pengjie Ren. 2024. Generative Retrieval as Multi-Vector Dense Retrieval. In Proceedings of the 47th International ACM Conference on Research and Development in Information Retrieval, SIGIR, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 1828–1838. DOI: 10.1145/3626772.3657697
J. Xie, S. Liu, G. Cong, and Z. Chen. 2024. UnifiedSSR: A unified framework of sequential search and recommendation. In Proceedings of the ACM Web Conference 2024. 3410–3419.
Hamed Zamani and W. Bruce Croft. 2018. Joint Modeling and Optimization of Search and Recommendation. [link]
Hamed Zamani and W. Bruce Croft. 2020. Learning a Joint Search and Recommendation Model from User-Item Interactions. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM’20). 717–725. DOI: 10.1145/3336191.3371818
Hamed Zamani and W. Bruce Croft. 2020. Learning a Joint Search and Recommendation Model from User-Item Interactions. In Proceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM ’20). Association for Computing Machinery, New York, NY, USA, 717–725. DOI: 10.1145/3336191.3371818
H. Zeng, S. Kallumadi, Z. Alibadi, R. Nogueira, and H. Zamani. 2023. A Personalized Dense Retrieval Framework for Unified Information Access, See [5], 121–130.
K. Zhao, Y. Zheng, T. Zhuang, X. Li, and X. Zeng. 2023. Joint Learning of E-commerce Search and Recommendation with a Unified Graph Neural Network, See [5], 1461–1469.
Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, and Maarten de Rijke. 2024. Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. In Proceedings of the 47th International ACM Conference on Research and Development in Information Retrieval, SIGIR, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 1796–1806. DOI: 10.1145/3626772.3657828
Nicholas J. Belkin and W. Bruce Croft. 1992. Information filtering and information retrieval: two sides of the same coin? Commun. ACM 35 (1992), 29–38. [link]
Guilherme Bittencourt, Naan Vasconcelos, Yan Andrade, Nícollas Silva, Washington Cunha, Diego Roberto Colombo Dias, Marcos André Gonçalves, and Leonardo Rocha. 2026. Review-Aware Recommender Systems (RARSs): Recent Advances, Experimental Comparative Analysis, Discussions, and New Directions. ACM Comput. Surv. 58, 1 (2026), 21:1–21:49. DOI: 10.1145/3744661
Òscar Celma and Pedro Cano. 2008. From hits to niches? or how popular artists can bias music recommendation and discovery. In Proceedings of the 2nd KDD workshop on large-scale recommender systems and the netflix prize competition.
H.-H. Chen, W.-J. Duh, H.-H. Huang, M. P. Kato, J. Mothe, and B. Poblete (Eds.). 2023. Proc. 46th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023). ACM Press, New York, USA.
Murilo Cunha, Marilia Silveira, Brenda Santana, Larissa Freitas, and Ulisses Corrêa. 2025. Optimizing and Evaluating a Retrieval-Augmented Generation System for Normative Document Retrieval in Hospital Settings. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/ RJ). SBC, Porto Alegre, RS, Brasil, 385–393. DOI: 10.5753/webmedia.2025.16029
Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An Effective, Efficient, and Scalable Confidence-Based Instance Selection Framework for Transformer-Based Text Classification. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 665–674. DOI: 10.1145/3539618.3591638
Washington Cunha, Felipe Viegas, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2025. A Noise-Oriented and Redundancy-Aware Instance Selection Framework. ACM Transactions on Information Systems 43, 1 (2025). DOI: 10.1145/3701769
David Fernandes, Edleno Silva de Moura, Berthier A. Ribeiro-Neto, Altigran Soares da Silva, and Marcos André Gonçalves. 2007. Computing Block Importance for Searching on Web Sites. In Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management. ACM, 165–174. DOI: 10.1145/1321440.1321466
Shijie Geng, Shuchang Liu, Zuohui Fu, Yingqiang Ge, and Yongfeng Zhang. 2022. Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5). In Proceedings of the 16th ACM Conference on Recommender Systems (Seattle, WA, USA) (RecSys ’22). Association for Computing Machinery, New York, NY, USA, 299–315. DOI: 10.1145/3523227.3546767
Yuqi Gong, Xichen Ding, Yehui Su, Kaiming Shen, Zhongyi Liu, and Guannan Zhang. 2023. An Unified Search and Recommendation Foundation Model for Cold-Start Scenario. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21-25, 2023, Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, Min Zhang, and Rodrygo L. T. Santos (Eds.). ACM, 4595–4601. DOI: 10.1145/3583780.3614657
F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015).
Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, and Julian J. McAuley. 2025. Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining, WSDM 2025. 866–875. DOI: 10.1145/3701551.3703573
K. Järvelin and J. Kekäläinen. 2002. Cumulated Gain-Based Evaluation of IR Techniques. ACM Transactions on Information Systems (TOIS) 20, 4 (October 2002), 422–446.
Omar Khattab and Matei Zaharia. 2020. ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 39–48. DOI: 10.1145/3397271.3401075
Maciej Kula. 2015. Metadata Embeddings for User and Item Coldstart Recommendations. In Proceedings of the 2nd Workshop on New Trends on Content-Based Recommender Systems (CBRecSys 2015) (CEUR Workshop Proceedings, Vol. 1448). 14–21.
Yongqi Li, Xinyu Lin,WenjieWang, Fuli Feng, Liang Pang,Wenjie Li, Liqiang Nie, Xiangnan He, and Tat-Seng Chua. 2024. A Survey of Generative Search and Recommendation in the Era of Large Language Models. ArXiv abs/2404.16924 (2024). [link]
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations. CoRR abs/2102.10073 (2021). arXiv:2102.10073 [link]
Vinicius Machado, Murilo Schmitt, and Eduardo Spinosa. 2025. Extrapolation-Based Data Augmentation for Sequential Recommendation. In Proceedings of the 31st Brazilian Symposium on Multimedia and the Web (Rio de Janeiro/RJ). SBC, Porto Alegre, RS, Brasil, 238–247. DOI: 10.5753/webmedia.2025.15127
Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press.
S. Merlo, G. Faggioli, and N. Ferro. 2025. Joint Information Retrieval and Recommendation: a Reproducibility Study. In Proc. 33rd Italian Symposium on Advanced Database Systems (SEBD 2025), I. Bartolini, G. Cima, D. Firmani, D. Lembo, D. Martinenghi, M. Mecella, and A. Poggi (Eds.). CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, [link], 165–175.
Marc Najork. 2023. Generative Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2023, Taipei, Taiwan, July 23-27, 2023, Hsin-Hsi Chen, Wei-Jou (Edward) Duh, Hen-Hsen Huang, Makoto P. Kato, Josiane Mothe, and Barbara Poblete (Eds.). ACM, 1. DOI: 10.1145/3539618.3591871
Michael J. Pazzani and Daniel Billsus. 2007. Content-Based Recommendation Systems. In The Adaptive Web. Springer, 325–341.
Gustavo Penha, Ali Vardasbi, Enrico Palumbo, Marco de Nadai, and Hugues Bouchard. 2024. Bridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?. In Proceedings of the 18th ACM Conference on Recommender Systems. DOI: 10.1145/3640457.3688123
Stephen Robertson and Hugo Zaragoza. 2009. The Probabilistic Relevance Framework: BM25 and Beyond. Foundations and Trends in Information Retrieval 3, 4 (2009), 333–389. DOI: 10.1561/1500000019
Weichen Shen. 2017. DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models. [link].
Teng Shi, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Yang Song, and Enyun Yu. 2025. Unified Generative Search and Recommendation. arXiv preprint arXiv:2504.05730 (2025).
Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang Song, Kun Gai, and Ji-Rong Wen. 2023. When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 1313–1323. DOI: 10.1145/3539618.3591786
Thiago Silva, Nícollas Silva, Heitor Werneck, Carlos Mito, Adriano CM Pereira, and Leonardo Rocha. 2022. Irec: An interactive recommendation framework. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 3165–3175.
Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Jiang. 2019. BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM international conference on information and knowledge management. 1441–1450.
Juntao Tan, Shuyuan Xu, Wenyue Hua, Yingqiang Ge, Zelong Li, and Yongfeng Zhang. 2024. IDGenRec: LLM-RecSys Alignment with Textual ID Learning. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2024, Washington DC, USA, July 14-18, 2024, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 355–364. DOI: 10.1145/3626772.3657821
Ash Vardanian. 2023. USearch by Unum Cloud. DOI: 10.5281/zenodo.7949416
Felipe Viegas, Washington Cunha, Christian Gomes, Antônio Pereira, Leonardo Rocha, and Marcos Goncalves. 2020. CluHTM: Semantic Hierarchical Topic Modeling Based on CluWords. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 8138–8150. DOI: 10.18653/v1/2020.acl-main.724
Jesse Vig, Shilad Sen, and John Riedl. 2012. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction. ACM Trans. Interact. Intell. Syst. 2, 3, Article 13 (Sept. 2012), 44 pages. DOI: 10.1145/2362394.2362395
Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and FuruWei. 2024. Multilingual E5 Text Embeddings: A Technical Report. arXiv preprint arXiv:2402.05672 (2024).
Qianyi Wang. 2025. Comparison of Different Recommendation Models based on Amazon Platform Dataset. In Proceedings of the 2024 5th International Conference on Computer Science and Management Technology (ICCSMT ’24). Association for Computing Machinery, New York, NY, USA, 74–79. DOI: 10.1145/3708036.3708048
Wei Wei, Xubin Ren, Jiabin Tang, Qinyong Wang, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024. LLMRec: Large Language Models with Graph Augmentation for Recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining, WSDM 2024, Merida, Mexico, March 4-8, 2024, Luz Angelica Caudillo-Mata, Silvio Lattanzi, Andrés Muñoz Medina, Leman Akoglu, Aristides Gionis, and Sergei Vassilvitskii (Eds.). ACM, 806–815. DOI: 10.1145/3616855.3635853
Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen, Jun Ma, Zhaochun Ren, Maarten de Rijke, and Pengjie Ren. 2024. Generative Retrieval as Multi-Vector Dense Retrieval. In Proceedings of the 47th International ACM Conference on Research and Development in Information Retrieval, SIGIR, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 1828–1838. DOI: 10.1145/3626772.3657697
J. Xie, S. Liu, G. Cong, and Z. Chen. 2024. UnifiedSSR: A unified framework of sequential search and recommendation. In Proceedings of the ACM Web Conference 2024. 3410–3419.
Hamed Zamani and W. Bruce Croft. 2018. Joint Modeling and Optimization of Search and Recommendation. [link]
Hamed Zamani and W. Bruce Croft. 2020. Learning a Joint Search and Recommendation Model from User-Item Interactions. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM’20). 717–725. DOI: 10.1145/3336191.3371818
Hamed Zamani and W. Bruce Croft. 2020. Learning a Joint Search and Recommendation Model from User-Item Interactions. In Proceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM ’20). Association for Computing Machinery, New York, NY, USA, 717–725. DOI: 10.1145/3336191.3371818
H. Zeng, S. Kallumadi, Z. Alibadi, R. Nogueira, and H. Zamani. 2023. A Personalized Dense Retrieval Framework for Unified Information Access, See [5], 121–130.
K. Zhao, Y. Zheng, T. Zhuang, X. Li, and X. Zeng. 2023. Joint Learning of E-commerce Search and Recommendation with a Unified Graph Neural Network, See [5], 1461–1469.
Yuyue Zhao, Jiancan Wu, Xiang Wang, Wei Tang, Dingxian Wang, and Maarten de Rijke. 2024. Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning. In Proceedings of the 47th International ACM Conference on Research and Development in Information Retrieval, SIGIR, Grace Hui Yang, Hongning Wang, Sam Han, Claudia Hauff, Guido Zuccon, and Yi Zhang (Eds.). ACM, 1796–1806. DOI: 10.1145/3626772.3657828
Publicado
27/10/2026
Como Citar
LIMA, André; GRECO, Davi; BERNARDES, Lucas Eduardo; FERES, Messias; GONÇALVES, Marcos André; ROCHA, Leonardo; VIEGAS, Felipe.
Tudo é Ranking! Adaptações Cruzadas entre Busca e Recomendação. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 32. , 2026, Lavras/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 489-498.
DOI: https://doi.org/10.5753/webmedia.2026.30195.
