iRev: A framework for evaluating recommender systems based on textual comments
DOI:
https://doi.org/10.5753/reic.2025.6040Keywords:
Review Aware, Recommendation Systems, Comparative EvaluationAbstract
Current advances in Recommendation Systems and Natural Language Processing have motivated recent studies to return their interest in Review-Aware Recommendation Systems (RARSs). In this sense, we employ a systematic mapping approach by selecting 117 papers published on the main vehicles of the area, presenting a summary of the advances, highlighting the main proposal algorithms, and detailing the most used datasets and metrics in experimental setups. All the implementations and other artifacts extracted from this study were consolidated into a framework: iREV. In addition, we conduct a comprehensive experimental comparison among state-of-the-art proposals, highlighting the main directions and new perspectives for future developments.
Downloads
References
Bittencourt, G., Fonseca, G., Andrade, Y., Silva, N., and Rocha, L. (2023). A survey on review - aware recommendation systems. In Proceedings of the 29th Brazilian Symposium on Multimedia and the Web, WebMedia ’23. DOI: 10.1145/3617023.3617050.
Chen, C., Zhang, M., Liu, Y., and Ma, S. (2018). Neural attentional rating regression with review-level explanations. In Proceedings of the 2018 World Wide Web Conference, pages 1583–1592. DOI: 10.1145/3178876.3186070.
Chin, J. Y., Zhao, K., Joty, S., and Cong, G. (2018). Anr: Aspect-based neural recommender. In 27th ACM CIKM, pages 147–156. DOI: 10.1145/3269206.3271810.
Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., and Wu, L. (2019). A capsule network for recommendation and explaining what you like and dislike. In 42nd ACM SIGIR, pages 275–284. DOI: 10.1145/3331184.3331216.
Liu, D., Li, J., Du, B., Chang, J., and Gao, R. (2019). Daml: Dual attention mutual learning between ratings and reviews for item recommendation. In 25th ACM SIGKDD, pages 344–352. DOI: 10.1145/3292500.3330906.
Liu, H., Wang, Y., Peng, Q., Wu, F., Gan, L., Pan, L., and Jiao, P. (2020). Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing, 374:77–85. DOI: 10.1016/j.neucom.2019.09.052.
Liu, Y., Yang, S., Zhang, Y., Miao, C., Nie, Z., and Zhang, J. (2021). Learning hierarchical review graph representations for recommendation. IEEE TKDE, 35(1):658–671. DOI: 10.1109/TKDE.2021.3075052.
Seo, S., Huang, J., Yang, H., and Liu, Y. (2017). Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In 11st ACM RecSys, pages 297–305. DOI: 10.1145/3109859.3109890.
Tay, Y., Luu, A. T., and Hui, S. (2018). Multi-pointer co-attention networks for recommendation. In 24th ACM SIGKDD, pages 2309–2318. DOI: 10.1145/3219819.3220086.
Werneck, H., Silva, N., Viana, M. C., Mourão, F., Pereira, A. C., and Rocha, L. (2020). A survey on point-of-interest recommendation in location-based social networks. In Proceedings of the Brazilian Symposium on Multimedia and the Web, pages 185–192. DOI: 10.1145/3428658.3430970.
Wu, L., Quan, C., Li, C., Wang, Q., Zheng, B., and Luo, X. (2019). A context-aware user-item representation learning for item recommendation. ACM TOIS, 37(2):1–29. DOI: 10.1145/3298988.
Zheng, L., Noroozi, V., and Yu, P. (2017). Joint deep modeling of users and items using reviews for recommendation. In ACM WSDM, pages 425–434. DOI: 10.1145/3018661.3018665.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 The authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
