Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa
Resumo
Recommender systems are increasingly leveraging textual reviews to enhance user preference modeling. Although review-aware recommender systems (RARs) have shown advances in capturing semantic nuances, they often fall short in representing collaborative patterns among users and items. In contrast, traditional collaborative filtering methods, though limited in semantic modeling, are robust in exploiting structural relationships. In this study, we propose a hybrid approach that integrates semantic representations derived from RARs into traditional collaborative models. We evaluated this strategy across three Amazon datasets, comparing RARs, collaborative, and hybrid models in terms of preference modeling accuracy (MSE and MAE), and recommendation relevance (Hits, Precision, Recall). Our results showthat hybrid models outperform both baselines, achieving up to 2.8 times lower prediction error and 60% higher precision. These findings highlight the potential of hybrid models to deliver more robust and context-aware recommendations.
Palavras-chave:
Review Aware, Reccomedation System, Colaborative Filtering
Referências
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Chien Chin Chen, Shun-Yuan Shih, and Meng Lee. 2016. Who should you follow? Combining learning to rank with social influence for informative friend recommendation. Decision Support Systems 90 (2016), 33–45.
Zhiyong Cheng, Ying Ding, Xiangnan He, Lei Zhu, Xuemeng Song, and Mohan Kankanhalli. 2018. A3NCF: an adaptive aspect attention model for rating prediction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (Stockholm, Sweden) (IJCAI’18). AAAI Press, 3748–3754.
Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan Kankanhalli. 2018. Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 639–648. DOI: 10.1145/3178876.3186145
Jin Yao Chin, Kaiqi Zhao, Shafiq Joty, and Gao Cong. 2018. ANR: Aspect-based Neural Recommender. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM ’18). Association for Computing Machinery, New York, NY, USA, 147–156. DOI: 10.1145/3269206.3271810
Dan Cosley, Shyong K Lam, Istvan Albert, Joseph A Konstan, and John Riedl. 2003. Is seeing believing? How recommender system interfaces affect users’ opinions. In Proceedings of the SIGCHI conference on Human factors in computing systems. 585–592.
Kostadin Cvejoski, Ramsés J. Sánchez, Christian Bauckhage, and César Ojeda. 2022. Dynamic Review-based Recommenders. In Data Science – Analytics and Applications, Peter Haber, Thomas J. Lampoltshammer, Helmut Leopold, and Manfred Mayr (Eds.). Springer Fachmedien Wiesbaden, Wiesbaden, 66–71.
Christian Desrosiers and George Karypis. 2011. A Comprehensive Survey of Neighborhood-based Recommendation Methods. Springer US, Boston, MA, 107–144. DOI: 10.1007/978-0-387-85820-3_4
Maurizio Ferrari Dacrema, Paolo Cremonesi, and Dietmar Jannach. 2019. Are we really making much progress? A worrying analysis of recent neural recommendation approaches. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 101–109. DOI: 10.1145/3298689.3347058
Jingyue Gao, Yang Lin, Yasha Wang, Xiting Wang, Zhao Yang, Yuanduo He, and Xu Chu. 2020. Set-Sequence-Graph: A Multi-View Approach Towards Exploiting Reviews for Recommendation. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (Virtual Event, Ireland) (CIKM ’20). Association for Computing Machinery, New York, NY, USA, 395–404. DOI: 10.1145/3340531.3411939
Negar Hariri, Bamshad Mobasher, Robin Burke, and Yong Zheng. 2011. Context-Aware Recommendation Based On Review Mining. In Proceedings of the 9th Workshop on Intelligent Techniques for Web Personalization & Recommender Systems, ITWP@IJCAI 2011, Barcelona, Spain, July 16, 2011 (CEUR Workshop Proceedings, Vol. 756), Sarabjot Singh Anand and Dietmar Jannach (Eds.). CEUR-WS.org. [link]
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Armand Joulin, Edouard Grave, and Piotr Bojanowski Tomas Mikolov. 2017. Bag of Tricks for Efficient Text Classification. EACL 2017 (2017), 427.
Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. 2016. Convolutional Matrix Factorization for Document Context-Aware Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 233–240. DOI: 10.1145/2959100.2959165
Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. Computer 42, 8 (2009), 30–37. DOI: 10.1109/MC.2009.263
Balraj Kumar and Neeraj Sharma. 2016. Approaches, Issues and Challenges in Recommender Systems: A Systematic Review. Indian Journal of Science and Technology 9 (2016), 47.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL). 7871–7880.
Chenliang Li, Cong Quan, Li Peng, Yunwei Qi, Yuming Deng, and LibingWu. 2019. A Capsule Network for Recommendation and Explaining What You Like and Dislike. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, 275–284. DOI: 10.1145/3331184.3331216
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.
Donghua Liu, Jing Li, Bo Du, Jun Chang, and Rong Gao. 2019. DAML: Dual Attention Mutual Learning between Ratings and Reviews for Item Recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (Anchorage, AK, USA) (KDD ’19). Association for Computing Machinery, New York, NY, USA, 344–352. DOI: 10.1145/3292500.3330906
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Publicado
10/11/2025
Como Citar
ANDRADE, Yan; VASCONCELOS, Naan; TULER, Elisa; PEREIRA, Adriano; ROCHA, Leonardo.
Uma Abordagem Híbrida para Sistemas de Recomendação com Base em Avaliações Textuais e Filtragem Colaborativa. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 455-463.
DOI: https://doi.org/10.5753/webmedia.2025.14831.
