Abstract
Real recommendation scenarios have diverse and multi-modal features, which are often high-dimensional, sparse, and heterogeneous, making it difficult to learn. To improve model performance, many studies jointly model the semantic information of user and item reviews. However, due to the inherent changes in natural language processing, it has become a challenge to more accurately model the semantic interaction and matching relationship between user and item reviews. In addition, most of the previous work directly connects text representations and numerical features without considering their natural gap. In view of this, we propose a novel Pattern Matching and Information-aware Network (MIAN). Specifically, we design a matching network composed of global matching and specific matching, which is used to model the matching information between user and item reviews. The specific matching module uses the item description as a bridge to assist in modeling more fine-grained matching relationships. In addition, we construct an information perception layer to align the information between reviews and numerical features. Furthermore, we adopt a joint learning manner for better model training by employing a matching task fusion, which benefits from the prior matching knowledge. Comprehensive experiments on five real-world datasets show that MIAN outperforms the state-of-the-art methods.
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References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. -Based Syst. 46, 109–132 (2013)
Chen, C., Zhang, M., Liu, Y., Ma, S.: Neural attentional rating regression with review-level explanations. In: Proceedings of the 2018 World Wide Web Conference, pp. 1583–1592 (2018)
Covington, P., Adams, J., Sargin, E.: Deep neural networks for Youtube recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 191–198 (2016)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Guan, X.: Attentive aspect modeling for review-aware recommendation. ACM Trans. Inform. Syst. (TOIS) 37(3), 1–27 (2019)
He, X., Chua, T.S.: Neural factorization machines for sparse predictive analytics. In: Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 355–364 (2017)
Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 233–240 (2016)
Lakkaraju, H., McAuley, J., Leskovec, J.: What’s in a name? Understanding the interplay between titles, content, and communities in social media. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 7, pp. 311–320 (2013)
Liu, D., Li, J., Du, B., Chang, J., Gao, R.: 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, pp. 344–352 (2019)
Liu, P., Zhang, L., Gulla, J.A.: Multilingual review-aware deep recommender system via aspect-based sentiment analysis. ACM Trans. Inform. Syst. (TOIS) 39(2), 1–33 (2021)
Liu, Y., Yang, S., Zhang, Y., Miao, C., Nie, Z., Zhang, J.: Learning hierarchical review graph representations for recommendation. IEEE Trans. Knowl. Data Eng. (2021)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances Neural Information Processing Systems, vol. 20, pp. 1257–1264 (2007)
Peng, Q., Liu, H., Yu, Y., Xu, H., Dai, W., Jiao, P.: Mutual self attention recommendation with gated fusion between ratings and reviews. In: Nah, Y., Cui, B., Lee, S.-W., Yu, J.X., Moon, Y.-S., Whang, S.E. (eds.) DASFAA 2020. LNCS, vol. 12114, pp. 540–556. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59419-0_33
Qiu, Z., Wu, X., Gao, J., Fan, W.: U-BERT: pre-training user representations for improved recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4320–4327 (2021)
Seo, S., Huang, J., Yang, H., Liu, Y.: Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 297–305 (2017)
Sun, P., Wu, L., Zhang, K., Fu, Y., Hong, R., Wang, M.: Dual learning for explainable recommendation: towards unifying user preference prediction and review generation. In: Proceedings of The Web Conference 2020, pp. 837–847 (2020)
Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)
Wang, H., Wang, N., Yeung, D.Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)
Wu, C., Wu, F., Liu, J., Huang, Y.: Hierarchical user and item representation with three-tier attention for recommendation. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 1818–1826 (2019)
Wu, L., Quan, C., Li, C., Wang, Q., Zheng, B., Luo, X.: A context-aware user-item representation learning for item recommendation. ACM Trans. Inform. Syst. (TOIS) 37(2), 1–29 (2019)
Wu, S., Zhang, Y., Zhang, W., Bian, K., Cui, B.: Enhanced review-based rating prediction by exploiting aside information and user influence. Knowl. -Based Syst. 222, 107015 (2021)
Xie, F., Zheng, A., Chen, L., Zheng, Z.: Attentive meta-graph embedding for item recommendation in heterogeneous information networks. Knowl.-Based Syst. 211, 106524 (2021)
Xiong, K., et al.: Counterfactual review-based recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2231–2240 (2021)
Yang, W., Fan, X., Chen, Y., Li, F., Chang, H.: An effective implicit multi-interest interaction network for recommendation. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13111, pp. 680–691. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-92273-3_56
Yang, W., Hu, T.: DFCN: an effective feature interactions learning model for recommender systems. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12683, pp. 195–210. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73200-4_13
Yang, Z.: Generating knowledge-based explanation for recommendation from review. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, p. 3494 (2022)
Zhang, Y., Lai, G., Zhang, M., Zhang, Y., Liu, Y., Ma, S.: Explicit factor models for explainable recommendation based on phrase-level sentiment analysis. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 83–92 (2014)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. In: Proceedings of the tenth ACM International Conference on Web Search and Data Mining, pp. 425–434 (2017)
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Yang, W., Huo, T., Chen, Y., Liu, Z. (2022). Pattern Matching and Information-Aware Between Reviews and Ratings for Recommendation. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13631. Springer, Cham. https://doi.org/10.1007/978-3-031-20868-3_4
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