Abstract
Textual reviews of items are a popular resource of online recommendation. The semantic of reviews helps to achieve improved representation of users and items for recommendation. Current review-based recommender systems understand the semantic of reviews from a static view, i.e., independent of the specific user-item pair. However, the semantic of the reviews are personalized and context-aware, i.e., same reviews can have different semantics when they are written by different users or towards different items. Therefore, we propose an improved recommendation model by reconstructing multiple reviews into a personalized document. Given a user-item pair, we design a cross-attention model to build personalized documents by selecting important words in the reviews of the given user towards the given item and vice versa. A semantic encoder of personalized document is then designed using a cross-transformer mechanism to learn document-level representation of users and items. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed model.
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References
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2008, pp. 426–434. New York, NY, USA. Association for Computing Machinery (2008)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: Platt, J.C., Koller, D., Singer, Y., Roweis, S.T. (eds.), Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 3–6 December 2007, pp. 1257–1264. Curran Associates Inc (2007)
Bao, Y., Fang, H., Zhang, J.: Topicmf: simultaneously exploiting ratings and reviews for recommendation. In: Brodley, C.E., Stone, P. (eds.), Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 27–31 July 2014, Québec City, Québec, Canada, pp. 2–8. AAAI Press (2014)
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, WWW 2018, p. 1583C1592, Republic and Canton of Geneva, CHE 2018. International World Wide Web Conferences Steering Committee (2018)
Diao, Q., Qiu, M., Wu, C-Y., Smola, A.J., Jiang, J., Wang, C.: Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). In: Macskassy, S.A., Perlich, C., Leskovec, J., Wang, W., Ghani, R. (eds.), The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, USA - 24–27 August 2014, pp. 193–202. ACM (2014)
Li, C., Quan, C., Peng, L., Qi, Y., Deng, Y., Wu, L.: A capsule network for recommendation and explaining what you like and dislike. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J.-Y., Scholer, F. (eds.), Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 275–284. ACM (2019)
Liu, H., et al.: Hybrid neural recommendation with joint deep representation learning of ratings and reviews. Neurocomputing 374, 77–85 (2020)
Liu, H., et al.: NRPA: neural recommendation with personalized attention. In: Piwowarski, B., Chevalier, M., Gaussier, É., Maarek, Y., Nie, J.-Y., Scholer, F. (eds.), Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, Paris, France, 21–25 July 2019, pp. 1233–1236. ACM (2019)
Lu, Y., Dong, R., Smyth, B.: Coevolutionary recommendation model: mutual learning between ratings and reviews. In: Champin, P-A., Gandon, F., Lalmas, M., Ipeirotis, P.G. (eds.), Proceedings of the 2018 World Wide Web Conference on World Wide Web, WWW 2018, Lyon, France, 23–27 April 2018, pp. 773–782. ACM (2018)
Julian J. McAuley and Jure Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. In Qiang Yang, Irwin King, Qing Li, Pearl Pu, and George Karypis, editors, Seventh ACM Conference on Recommender Systems, RecSys ’13, Hong Kong, China, October 12–16, 2013, pages 165–172. ACM, 2013
Wang, X., et al.: Neural review rating prediction with hierarchical attentions and latent factors. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds.) DASFAA 2019. LNCS, vol. 11448, pp. 363–367. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18590-9_46
Ling, G., Lyu, M.R., King, I.: Ratings meet reviews, a combined approach to recommend. In: Kobsa, A., Zhou, M.X., Ester, M., Koren, Y. (eds.), Eighth ACM Conference on Recommender Systems, RecSys 2014, Foster City, Silicon Valley, CA, USA - 06–10 October 2014, pp. 105–112. ACM (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993C1022 (2003)
Zheng, L., Noroozi, V., Yu, P.S.: Joint deep modeling of users and items using reviews for recommendation. CoRR, abs/1701.04783 (2017)
Liu, D., Li, J., Du, B., Chang, J., Gao, R.: DAML: dual attention mutual learning between ratings and reviews for item recommendation. In: Teredesai, A., Kumar, V., Li, Y., Rosales, R., Terzi, E., Karypis, G. (eds.), Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4–8 August 2019, pp. 344–352. ACM (2019)
Liu, H., Wang, W., Peng, Q., Wu, N., Wu, F., Jiao, P.: Toward comprehensive user and item representations via three-tier attention network. ACM Trans. Inf. Syst. 39(3), 1–22 (2021)
Liu, H., Wang, W., Xu, H., Peng, Q., Jiao, P.: Neural unified review recommendation with cross attention. In: Huang, J., et al. (eds.), Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, SIGIR 2020, Virtual Event, China, 25–30 July 2020, pp. 1789–1792. ACM (2020)
Tay, Y., Luu, A.T., Hui, S.C.: Multi-pointer co-attention networks for recommendation. In: Guo, Y., Farooq, F., (eds.), Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, 19–23 August 2018, pp. 2309–2318. ACM (2018)
Tan, Y., Zhang, M., Liu, Y., Ma, S.: Rating-boosted latent topics: understanding users and items with ratings and reviews. In: Kambhampati, S. (ed), Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9–15 July 2016, pp. 2640–2646. IJCAI/AAAI Press (2016)
Seo, S., Huang, J., Yang, H., Liu, Y.: Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Cremonesi, P., Ricci, F., Berkovsky, S., Tuzhilin, A. (eds.), Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys 2017, Como, Italy, 27–31 August 2017, pp. 297–305. ACM (2017)
Dong, X., et al.: Asymmetrical hierarchical networks with attentive interactions for interpretable review-based recommendation. In :The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, New York, NY, USA, 7–12 February 2020, pp. 7667–7674. AAAI Press (2020)
Kim, D.H., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation. In: Sen, S., Geyer, W., Freyne, J., Castells, P. (eds.), Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 15–19 September 2016, pp. 233–240. ACM (2016)
Chin, J.Y., Zhao, K., Joty, S.R., Cong, G.: ANR: aspect-based neural recommender. In: Cuzzocrea, A., et al. (eds.) Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, 22–26 October 2018, pp. 147–156. ACM (2018)
Acknowledgement
This work was supported by a grant from the National Key Research and Development Program of China (2018YFC0809804), State Key Laboratory of Communication Content Cognition (Grant No. A32003), the Artificial Intelligence for Sustainable Development Goals (AI4SDGs) Research Program, National Natural Science Foundation of China (U1736103, 61976154, 61402323, 61876128), the National Key Research and Development Program (2017YFE0111900).
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Huang, Z., Wang, B., Liu, H., Jiang, Q., Xiong, N., Hou, Y. (2021). Improving Recommender System via Personalized Reconstruction of Reviews. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-030-92635-9_17
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DOI: https://doi.org/10.1007/978-3-030-92635-9_17
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