Integrando Avaliações Textuais de Usuários em Recomendação baseada em Aprendizado por Reforço
Resumo
In Multi-Armed-Bandit (MAB) approaches for Recommendation Systems, items are represented as arms to be recommended and the goal is to maximize the expected user’s satisfaction (i.e., reward). Despite the reward often being the ratings explicitly assigned by the user, in other scenarios, implicit ratings extracted from user comments by review-aware recommendation systems (RARs) may efficiently elucidate the user’s preferences. In this paper, we provide a preliminary study of the impact of using these implicit ratings instead of explicit ones in MAB approaches. Our results point out that implicit ratings decrease the entropy of the datasets, negatively impacting the performance of MAB.
Palavras-chave:
Sistema de recomendação consciente de comentário textual, MAB
Referências
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Yan Andrade, Nícollas Silva, Thiago Silva, Adriano C. M. Pereira, Diego Roberto Colombo Dias, Elisa Tuler de Albergaria, and Leonardo Rocha. 2023. A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems. In Proceedings of the 29th Brazilian Symposium on Multimedia. DOI: 10.1145/3617023.3617049
Andrea Barraza-Urbina and Dorota Glowacka. 2020. Introduction to Bandits in Recommender Systems. In Fourteenth ACM RecSys. 748–750.
Guilherme Bittencourt, Guilherme Fonseca, Yan Andrade, Nícollas Silva, and Leonardo Rocha. 2023. A Survey on Review - Aware Recommendation Systems. In Proceedings of the 29th Brazilian Symposium on Multimedia. DOI: 10.1145/3617023.3617050
Yijin Cai, Yilei Wang, Weijin Wang, and Wenting Chen. 2022. RI-GCN: Reviewaware Interactive Graph Convolutional Network for Review-based Item Recommendation. In 2022 IEEE International Conference on Big Data (Big Data).
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to rank features for recommendation over multiple categories. In SIGIR.
Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based neural recommender. In CIKM.
Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An effective, efficient, and scalable confidencebased instance selection framework for transformer-based text classification. InSIGIR.
Claudio MV de Andrade, Fabiano M Belém, Washington Cunha, Celso França, Felipe Viegas, Leonardo Rocha, and Marcos André Gonçalves. 2023. On the class separability of contextual embeddings representations–or “The classifier does not matter when the (text) representation is so good!”. IP&M (2023).
Vinicius HS Durelli, Rafael S Durelli, Andre T Endo, Elder Cirilo, Washington Luiz, and Leonardo Rocha. 2018. Please please me: does the presence of test cases influence mobile app users’ satisfaction?. In Proceedings of the XXXII Brazilian Symposium on Software Engineering.
Lucas GS Félix, João Victor Silveira, Washington Luiz, Diego Dias, and Leonardo Rocha. 2018. Avaliação Automática de Conteúdo de Aplicações de Reclamação Online. In Anais do VI Symposium on Knowledge Discovery, Mining and Learning. SBC, 49–56.
Balraj Kumar and Neeraj Sharma. 2016. Approaches, issues and challenges in recommender systems: a systematic review. Indian J. Sci. Technol 9, 47 (2016), 1–12.
Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019. A capsule network for recommendation and explaining what you like and dislike. In SIGIR.
Duantengchuan Li, Hai Liu, Zhaoli Zhang, Ke Lin, Shuai Fang, Zhifei Li, and Neal N Xiong. 2021. CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms. Neurocomputing 455 (2021), 283–296.
Yanghao Li, Cuiling Lan, Junliang Xing, Wenjun Zeng, Chunfeng Yuan, and Jiaying Liu. 2016. Online human action detection using joint classification-regression recurrent neural networks. In European Conference on Computer Vision. Springer, 203–220.
Hongtao Liu, Yian Wang, Qiyao Peng, Fangzhao Wu, Lin Gan, Lin Pan, and Pengfei Jiao. 2020. Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374 (2020), 77–85.
Washington Luiz, Felipe Viegas, Rafael Odon de Alencar, Fernando Mourão, Thiago Salles, Dárlinton B. F. Carvalho, Marcos André Gonçalves, and Leonardo Rocha. 2018. A Feature-Oriented Sentiment Rating for Mobile App Reviews. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018. ACM.
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Javier Sanz-Cruzado, Pablo Castells, and Esther López. 2019. A simple multiarmed nearest-neighbor bandit for interactive recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 358–362.
Nícollas Silva, Heitor Werneck, Thiago Silva, Adriano C. M. Pereira, and Leonardo Rocha. 2021. A contextual approach to improve the user’s experience in interactive recommendation systems. In WebMedia ’21: Brazilian Symposium on Multimedia and the Web, Belo Horizonte, Minas Gerais, Brazil, November 5-12, 2021, Adriano César Machado Pereira and Leonardo Chaves Dutra da Rocha (Eds.). ACM, 89–96. DOI: 10.1145/3470482.3479621
Thiago Silva, Nícollas Silva, Carlos Mito, Adriano C. M. Pereira, and Leonardo Rocha. 2022. Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario. In WebMedia’22. DOI: 10.1145/3539637.3557060
Thiago Silva, Nícollas Silva, Heitor Werneck, Adriano CM Pereira, and Leonardo Rocha. 2020. The impact of first recommendations based on exploration or exploitation approaches in recommender systems’ learning. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 173–180.
Mehdi Srifi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline. 2020. Recommender systems based on collaborative filtering using review texts—a survey. Information 11, 6 (2020), 317.
Yunzhi Tan, Min Zhang, Yiqun Liu, and Shaoping Ma. 2016. Rating-boosted latent topics: Understanding users and items with ratings and reviews.. In IJCAI, Vol. 16. 2640–2646.
Huazheng Wang, Qingyun Wu, and Hongning Wang. 2016. Learning hidden features for contextual bandits. In CIKM. 1633–1642.
Huazheng Wang, Qingyun Wu, and Hongning Wang. 2017. Factorization bandits for interactive recommendation. In Thirty-First AAAI Conference on Artificial Intelligence.
Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S Sitharama Iyengar, Larisa Shwartz, and Genady Ya Grabarnik. 2018. Online interactive collaborative filtering using multi-armed bandit with dependent arms. TKDE (2018).
Peilin Yang, Yingyuan Xiao, Wenguang Zheng, Xu Jiao, Ke Zhu, Chenchen Sun, and Li Liu. 2023. MAN: Main-auxiliary network with attentive interactions for review-based recommendation. Applied Intelligence 53, 10 (2023), 12955–12970.
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-aware self-supervised tri-training for recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2084–2092.
Bruna Stella Zanotto, Ana Paula Beck da Silva Etges, Avner Dal Bosco, Eduardo Gabriel Cortes, Renata Ruschel, Ana Claudia De Souza, Claudio MV Andrade, Felipe Viegas, Sergio Canuto, Washington Luiz, et al. 2021. Stroke outcome measurements from electronic medical records: cross-sectional study on the effectiveness of neural and nonneural classifiers. JMIR Medical Informatics (2021).
Chenyan Zhang, Shan Xue, Jing Li, Jia Wu, Bo Du, Donghua Liu, and Jun Chang. 2023. Multi-Aspect enhanced Graph Neural Networks for recommendation. Neural Networks 157 (2023), 90–102.
Xiaoxue Zhao, Weinan Zhang, and Jun Wang. 2013. Interactive collaborative filtering. In CIMK.
Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu. 2020. Interactive recommender system via knowledge graph-enhanced reinforcement learning. In SIGIR. 179–188.
Yan Andrade, Nícollas Silva, Thiago Silva, Adriano C. M. Pereira, Diego Roberto Colombo Dias, Elisa Tuler de Albergaria, and Leonardo Rocha. 2023. A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems. In Proceedings of the 29th Brazilian Symposium on Multimedia. DOI: 10.1145/3617023.3617049
Andrea Barraza-Urbina and Dorota Glowacka. 2020. Introduction to Bandits in Recommender Systems. In Fourteenth ACM RecSys. 748–750.
Guilherme Bittencourt, Guilherme Fonseca, Yan Andrade, Nícollas Silva, and Leonardo Rocha. 2023. A Survey on Review - Aware Recommendation Systems. In Proceedings of the 29th Brazilian Symposium on Multimedia. DOI: 10.1145/3617023.3617050
Yijin Cai, Yilei Wang, Weijin Wang, and Wenting Chen. 2022. RI-GCN: Reviewaware Interactive Graph Convolutional Network for Review-based Item Recommendation. In 2022 IEEE International Conference on Big Data (Big Data).
Xu Chen, Zheng Qin, Yongfeng Zhang, and Tao Xu. 2016. Learning to rank features for recommendation over multiple categories. In SIGIR.
Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based neural recommender. In CIKM.
Washington Cunha, Celso França, Guilherme Fonseca, Leonardo Rocha, and Marcos André Gonçalves. 2023. An effective, efficient, and scalable confidencebased instance selection framework for transformer-based text classification. InSIGIR.
Claudio MV de Andrade, Fabiano M Belém, Washington Cunha, Celso França, Felipe Viegas, Leonardo Rocha, and Marcos André Gonçalves. 2023. On the class separability of contextual embeddings representations–or “The classifier does not matter when the (text) representation is so good!”. IP&M (2023).
Vinicius HS Durelli, Rafael S Durelli, Andre T Endo, Elder Cirilo, Washington Luiz, and Leonardo Rocha. 2018. Please please me: does the presence of test cases influence mobile app users’ satisfaction?. In Proceedings of the XXXII Brazilian Symposium on Software Engineering.
Lucas GS Félix, João Victor Silveira, Washington Luiz, Diego Dias, and Leonardo Rocha. 2018. Avaliação Automática de Conteúdo de Aplicações de Reclamação Online. In Anais do VI Symposium on Knowledge Discovery, Mining and Learning. SBC, 49–56.
Balraj Kumar and Neeraj Sharma. 2016. Approaches, issues and challenges in recommender systems: a systematic review. Indian J. Sci. Technol 9, 47 (2016), 1–12.
Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and Libing Wu. 2019. A capsule network for recommendation and explaining what you like and dislike. In SIGIR.
Duantengchuan Li, Hai Liu, Zhaoli Zhang, Ke Lin, Shuai Fang, Zhifei Li, and Neal N Xiong. 2021. CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms. Neurocomputing 455 (2021), 283–296.
Yanghao Li, Cuiling Lan, Junliang Xing, Wenjun Zeng, Chunfeng Yuan, and Jiaying Liu. 2016. Online human action detection using joint classification-regression recurrent neural networks. In European Conference on Computer Vision. Springer, 203–220.
Hongtao Liu, Yian Wang, Qiyao Peng, Fangzhao Wu, Lin Gan, Lin Pan, and Pengfei Jiao. 2020. Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374 (2020), 77–85.
Washington Luiz, Felipe Viegas, Rafael Odon de Alencar, Fernando Mourão, Thiago Salles, Dárlinton B. F. Carvalho, Marcos André Gonçalves, and Leonardo Rocha. 2018. A Feature-Oriented Sentiment Rating for Mobile App Reviews. In Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018. ACM.
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 1532–1543.
Javier Sanz-Cruzado, Pablo Castells, and Esther López. 2019. A simple multiarmed nearest-neighbor bandit for interactive recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 358–362.
Nícollas Silva, Heitor Werneck, Thiago Silva, Adriano C. M. Pereira, and Leonardo Rocha. 2021. A contextual approach to improve the user’s experience in interactive recommendation systems. In WebMedia ’21: Brazilian Symposium on Multimedia and the Web, Belo Horizonte, Minas Gerais, Brazil, November 5-12, 2021, Adriano César Machado Pereira and Leonardo Chaves Dutra da Rocha (Eds.). ACM, 89–96. DOI: 10.1145/3470482.3479621
Thiago Silva, Nícollas Silva, Carlos Mito, Adriano C. M. Pereira, and Leonardo Rocha. 2022. Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario. In WebMedia’22. DOI: 10.1145/3539637.3557060
Thiago Silva, Nícollas Silva, Heitor Werneck, Adriano CM Pereira, and Leonardo Rocha. 2020. The impact of first recommendations based on exploration or exploitation approaches in recommender systems’ learning. In Proceedings of the Brazilian Symposium on Multimedia and the Web. 173–180.
Mehdi Srifi, Ahmed Oussous, Ayoub Ait Lahcen, and Salma Mouline. 2020. Recommender systems based on collaborative filtering using review texts—a survey. Information 11, 6 (2020), 317.
Yunzhi Tan, Min Zhang, Yiqun Liu, and Shaoping Ma. 2016. Rating-boosted latent topics: Understanding users and items with ratings and reviews.. In IJCAI, Vol. 16. 2640–2646.
Huazheng Wang, Qingyun Wu, and Hongning Wang. 2016. Learning hidden features for contextual bandits. In CIKM. 1633–1642.
Huazheng Wang, Qingyun Wu, and Hongning Wang. 2017. Factorization bandits for interactive recommendation. In Thirty-First AAAI Conference on Artificial Intelligence.
Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S Sitharama Iyengar, Larisa Shwartz, and Genady Ya Grabarnik. 2018. Online interactive collaborative filtering using multi-armed bandit with dependent arms. TKDE (2018).
Peilin Yang, Yingyuan Xiao, Wenguang Zheng, Xu Jiao, Ke Zhu, Chenchen Sun, and Li Liu. 2023. MAN: Main-auxiliary network with attentive interactions for review-based recommendation. Applied Intelligence 53, 10 (2023), 12955–12970.
Junliang Yu, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. 2021. Socially-aware self-supervised tri-training for recommendation. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2084–2092.
Bruna Stella Zanotto, Ana Paula Beck da Silva Etges, Avner Dal Bosco, Eduardo Gabriel Cortes, Renata Ruschel, Ana Claudia De Souza, Claudio MV Andrade, Felipe Viegas, Sergio Canuto, Washington Luiz, et al. 2021. Stroke outcome measurements from electronic medical records: cross-sectional study on the effectiveness of neural and nonneural classifiers. JMIR Medical Informatics (2021).
Chenyan Zhang, Shan Xue, Jing Li, Jia Wu, Bo Du, Donghua Liu, and Jun Chang. 2023. Multi-Aspect enhanced Graph Neural Networks for recommendation. Neural Networks 157 (2023), 90–102.
Xiaoxue Zhao, Weinan Zhang, and Jun Wang. 2013. Interactive collaborative filtering. In CIMK.
Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu. 2020. Interactive recommender system via knowledge graph-enhanced reinforcement learning. In SIGIR. 179–188.
Publicado
14/10/2024
Como Citar
VASCONCELOS, Naan; REIS, Davi; SILVA, Thiago; SILVA, Nícollas; CUNHA, Washington; TULER, Elisa; ROCHA, Leonardo.
Integrando Avaliações Textuais de Usuários em Recomendação baseada em Aprendizado por Reforço. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 30. , 2024, Juiz de Fora/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 390-394.
DOI: https://doi.org/10.5753/webmedia.2024.241405.
