A Survey on Review - Aware Recommendation Systems
Resumo
Current advances in Deep Neural Networks have motivated recent studies to return their interest in Review-Aware Recommender Systems (RSs). In this sense, we employ a systematic mapping approach by selecting 56 papers published on the main vehicles of the area, such as RecSys, VLDB, SIGIR, and others. Reading these works and synthesising their achievements, we provide an updated picture of this field by highlighting relevant outcomes, contributions, and limitations. Especially, we have identified two huge limitations in the area. First, many of these works do not provide their code sources. Second, there is a prevalent tendency to prioritize accuracy above other quality dimensions, despite the consensus within the community to assess the practical effectiveness of RSs through many metrics. Both observations create constraints that restrict reproducibility and hinder straightforward comparisons in the field. Addressing this gap, this work also provides an open-source library that compiles the principal strategies proposed in the literature. Furthermore, we conducted a comprehensive evaluation through several datasets and metrics. Our findings indicate that while no single algorithm demonstrates absolute superiority, RSs based on neural networks have particularly exhibited the most competitive performance.
Palavras-chave:
Review Aware, Recommendation Systems, Comparative Evaluation
Referências
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KR1442 Chowdhary and KR Chowdhary. 2020. Natural language processing. Fundamentals of artificial intelligence (2020), 603–649
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Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J Smola, Jing Jiang, and Chong Wang. 2014. Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 193–202.
Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, and Gerard De Melo. 2020. Asymmetrical hierarchical networks with attentive interactions for interpretable review-based recommendation. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 7667–7674
Xiangcheng Du, Tianlong Ma, Yingbin Zheng, Hao Ye, Xingjiao Wu, and Liang He. 2020. Scene text recognition with temporal convolutional encoder. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2383–2387
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. 395–404.
Xinyu Guan, Zhiyong Cheng, Xiangnan He, Yongfeng Zhang, Zhibo Zhu, Qinke Peng, and Tat-Seng Chua. 2019. Attentive aspect modeling for review-aware recommendation. ACM Transactions on Information Systems (TOIS) 37, 3 (2019), 1–27.
Siyuan Guo, Ying Wang, Hao Yuan, Zeyu Huang, Jianwei Chen, and Xin Wang. 2021. TAERT: triple-attentional explainable recommendation with temporal convolutional network. Information Sciences 567 (2021), 185–200
Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International conference on machine learning. PMLR, 4116–4126
Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. Trirank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM international on conference on information and knowledge management. 1661–1670.
Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. Nais: Neural attentive item similarity model for recommendation. IEEE Transactions on Knowledge and Data Engineering 30, 12 (2018), 2354–2366.
Jonathan L Herlocker, Joseph A Konstan, Loren G Terveen, and John T Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22, 1 (2004), 5–53.
Dongmin Hyun, Chanyoung Park, Junsu Cho, and Hwanjo Yu. 2021. Learning to utilize auxiliary reviews for recommendation. Information Sciences 545 (2021), 595–607
Yaru Jin, Shoubin Dong, Yong Cai, and Jinlong Hu. 2020. RACRec: review aware cross-domain recommendation for fully-cold-start user. IEEE Access 8 (2020), 55032–55041
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2016. Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In 2018 IEEE international conference on data mining (ICDM). IEEE, 197–206
Zahid Younas Khan, Zhendong Niu, Sulis Sandiwarno, and Rukundo Prince. 2021. Deep learning techniques for rating prediction: a survey of the state-of-the-art. Artificial Intelligence Review 54 (2021), 95–135.
Shah Khusro, Zafar Ali, and Irfan Ullah. 2016. Recommender systems: issues, challenges, and research opportunities. In Information science and applications (ICISA) 2016. Springer, 1179–1189
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. 233–240.
Donghyun Kim, Chanyoung Park, Jinoh Oh, and Hwanjo Yu. 2017. Deep hybrid recommender systems via exploiting document context and statistics of items. Information Sciences 417 (2017), 72–87.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
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 Proceedings of the 42nd ACM SIGIR. 275–284.
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Publicado
23/10/2023
Como Citar
BITTENCOURT, Guilherme; FONSECA, Guilherme; ANDRADE, Yan; SILVA, Nícollas; ROCHA, Leonardo.
A Survey on Review - Aware Recommendation Systems. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 198–207.
