{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:42:18Z","timestamp":1776811338772,"version":"3.51.2"},"reference-count":28,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2021,1,19]]},"abstract":"<jats:p>Reviews text reflects user interests and product characteristics, which provide rich useful semantic textual information for modeling user and product. Some existing works improve the performance of rating prediction by distinguishing the usefulness of each review. However, most of them ignore the fact that the usefulness of each review should be dynamic and dependent on the target user-product pair. For example, when we predict the user\u2019s ratings of a restaurant, the user\u2019s previous reviews about restaurants should be more useful. To be more specific, when we model the target user-product pair, the usefulness of each review is associated with the target user and item. To address the above issue, we use a Review-level Dynamic Topic Co-Attention, which combines all reviews written by the user and all reviews that were written for the product to assign attention score for each review collaboratively. Besides, we believe that product category and user co-purchase information can further improve the rating prediction performance. In this paper, we propose a neural joint model called NMRP based on reviews, products category, and users\u2019 co-purchase information for rating prediction recommendation. First, the Review Extraction Module learns user and product information from reviews. Then, HIN Extraction Module extracts the association feature for a given target user-product pair from a heterogeneous information network (HIN). Finally, the two parts of data are connected and input into the feature interaction method Attentional Factorization Machines (AFM), to achieve rating prediction. The experimental study on three datasets from Amazon shows that our model outperforms recently proposed baselines such as DeepCoNN, TransNets, and NARRE.<\/jats:p>","DOI":"10.3233\/jcm-204226","type":"journal-article","created":{"date-parts":[[2020,4,14]],"date-time":"2020-04-14T13:36:35Z","timestamp":1586871395000},"page":"1127-1142","source":"Crossref","is-referenced-by-count":0,"title":["A neural joint model for rating prediction recommendation"],"prefix":"10.66113","volume":"20","author":[{"given":"Jingfan","family":"Tang","sequence":"first","affiliation":[]},{"given":"Xiujie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xinqiang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Ming","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"55691","reference":[{"key":"10.3233\/JCM-204226_ref1","first-page":"30","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer (Long Beach Calif)"},{"issue":"1","key":"10.3233\/JCM-204226_ref2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","article-title":"Amazon.com recommendations: Item-to-item collaborative filtering","volume":"7","author":"Linden","year":"2003","journal-title":"IEEE Internet Comput"},{"key":"10.3233\/JCM-204226_ref3","unstructured":"A. Popescul, D.M. Pennock and S. Lawrence, Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments, in: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001), 437\u2013444."},{"issue":"6","key":"10.3233\/JCM-204226_ref4","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1007\/s10208-009-9045-5","article-title":"Exact matrix completion via convex optimization","volume":"9","author":"Cand\u00e8s","year":"2009","journal-title":"Found Comput Math"},{"key":"10.3233\/JCM-204226_ref5","doi-asserted-by":"crossref","unstructured":"C. Wang and D.M. Blei, Collaborative topic modeling for recommending scientific articles, in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2011), 448\u2013456.","DOI":"10.1145\/2020408.2020480"},{"key":"10.3233\/JCM-204226_ref6","doi-asserted-by":"crossref","unstructured":"J. McAuley and J. Leskovec, Hidden factors and hidden topics: Understanding rating dimensions with review text, in: Proceedings of the 7th ACM Conference on Recommender Systems (2013), 165\u2013172.","DOI":"10.1145\/2507157.2507163"},{"key":"10.3233\/JCM-204226_ref7","doi-asserted-by":"crossref","unstructured":"Y. Bao, H. Fang and J. Zhang, TopicMF: Simultaneously exploiting ratings and reviews for recommendation, in: Proceedings of the National Conference on Artificial Intelligence (2014), 2\u20138.","DOI":"10.1609\/aaai.v28i1.8715"},{"key":"10.3233\/JCM-204226_ref8","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J Mach Learn Res"},{"key":"10.3233\/JCM-204226_ref9","doi-asserted-by":"crossref","unstructured":"L. Zheng, V. Noroozi and P.S. Yu, 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 (2017), 425\u2013433.","DOI":"10.1145\/3018661.3018665"},{"key":"10.3233\/JCM-204226_ref10","doi-asserted-by":"crossref","unstructured":"S. Rendle, Factorization machines, in: 2010 IEEE International Conference on Data Mining (2010), 995\u20131000.","DOI":"10.1109\/ICDM.2010.127"},{"key":"10.3233\/JCM-204226_ref11","doi-asserted-by":"crossref","unstructured":"R. Catherine and W. Cohen, TransNets: Learning to transform for recommendation, in: Proceedings of the 11th ACM Conference on Recommender Systems (2017), 288\u2013296.","DOI":"10.1145\/3109859.3109878"},{"key":"10.3233\/JCM-204226_ref12","doi-asserted-by":"crossref","unstructured":"S. Seo, J. Huang, H. Yang and Y. Liu, Interpretable convolutional neural networks with dual local and global attention for review rating prediction, in: Proceedings of the Eleventh ACM Conference on Recommender Systems (2017), 297\u2013305.","DOI":"10.1145\/3109859.3109890"},{"key":"10.3233\/JCM-204226_ref13","doi-asserted-by":"crossref","unstructured":"C. Chen, M. Zhang, Y. Liu and S. Ma, Neural attentional rating regression with review-level explanations, in: Proceedings of the 2018 World Wide Web Conference (2018), 1583\u20131592.","DOI":"10.1145\/3178876.3186070"},{"key":"10.3233\/JCM-204226_ref14","doi-asserted-by":"crossref","unstructured":"Y. Lu, R. Dong and B. Smyth, Coevolutionary recommendation model: Mutual learning between ratings and reviews, in: Proceedings of the 2018 World Wide Web Conference (2018), 773\u2013782.","DOI":"10.1145\/3178876.3186158"},{"key":"10.3233\/JCM-204226_ref15","doi-asserted-by":"crossref","unstructured":"J. Xiao, H. Ye, X. He, H. Zhang, F. Wu and T.-S. Chua, Attentional factorization machines: Learning the weight of feature interactions via attention networks, in: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (2017), 3119\u20133125.","DOI":"10.24963\/ijcai.2017\/435"},{"key":"10.3233\/JCM-204226_ref16","unstructured":"L. Jiasen, Y. Jianwei, B. Dhruv and P. Devi, Hierarchical question-image co-attention for visual question answering, in: Advances in Neural Information Processing Systems (2016), 289\u2013297."},{"key":"10.3233\/JCM-204226_ref17","doi-asserted-by":"crossref","unstructured":"Z. Cheng, Y. Ding, L. Zhu and M. Kankanhalli, Aspect-aware latent factor model: Rating prediction with ratings and reviews, in: Proceedings of the 2018 World Wide Web Conference (2018), 639\u2013648.","DOI":"10.1145\/3178876.3186145"},{"key":"10.3233\/JCM-204226_ref18","unstructured":"Y. Tan, M. Zhang, Y. Liu and S. Ma, Rating-boosted latent topics: Understanding users and items with ratings and reviews, in: IJCAI International Joint Conference on Artificial Intelligence 16 (2016), 2640\u20132646."},{"key":"10.3233\/JCM-204226_ref19","doi-asserted-by":"crossref","unstructured":"Z. Cheng, Y. Ding, X. He, L. Zhu, X. Song and M. Kankanhalli, A 3NCF: An adaptive aspect attention model for rating prediction, in: International Joint Conference on Artificial Intelligence (2018), 3748\u20133754.","DOI":"10.24963\/ijcai.2018\/521"},{"key":"10.3233\/JCM-204226_ref20","doi-asserted-by":"crossref","unstructured":"Q. Diao, M. Qiu, C.Y. Wu, A.J. Smola, J. Jiang and C. Wang, Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS), in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2014), 193\u2013202.","DOI":"10.1145\/2623330.2623758"},{"key":"10.3233\/JCM-204226_ref21","doi-asserted-by":"crossref","unstructured":"X. He, L. Liao, H. Zhang, L. Nie, X. Hu and T.-S. Chua, Neural collaborative filtering, in: Proceedings of the 26th International Conference on World Wide Web (2017), 173\u2013182.","DOI":"10.1145\/3038912.3052569"},{"key":"10.3233\/JCM-204226_ref22","doi-asserted-by":"crossref","unstructured":"J.Y. Chin, S. Joty, K. Zhao and G. Cong, ANR: Aspect-based neural recommender, in: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (2018), 147\u2013156.","DOI":"10.1145\/3269206.3271810"},{"key":"10.3233\/JCM-204226_ref23","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"Mikolov","year":"2013","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.3233\/JCM-204226_ref24","doi-asserted-by":"crossref","unstructured":"K. Cho, C. Van Merri\u00ebnboer, B. Gulcehre, F. Bahdanau, D. Bougares, H. Schwenk and Y. Bengio, Learning phrase representations using RNN encoder-decoder for statistical machine translation, in: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (2014), 1724\u20131734.","DOI":"10.3115\/v1\/D14-1179"},{"key":"10.3233\/JCM-204226_ref25","doi-asserted-by":"crossref","unstructured":"Y. Sun, J. Han, X. Yan, P.S. Yu and T. Wu, Pathsim: Meta path-based top-k similarity search in heterogeneous information networks, Proc VLDB Endow 4(11) (2011), 992\u20131003.","DOI":"10.14778\/3402707.3402736"},{"issue":"1","key":"10.3233\/JCM-204226_ref26","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","article-title":"A survey of heterogeneous information network analysis","volume":"29","author":"Shi","year":"2017","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.3233\/JCM-204226_ref27","unstructured":"T.Y. Fu, W.C. Lee and Z. Lei, HIN2Vec: Explore meta-paths in heterogeneous information networks for representation learning, in: International Conference on Information and Knowledge Management, Proceedings (2017), 1797\u20131806."},{"key":"10.3233\/JCM-204226_ref28","first-page":"201","article-title":"Why does unsupervised pre-training help deep learning","volume":"9","author":"Erhan","year":"2010","journal-title":"J Mach Learn Res"}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-204226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:06:04Z","timestamp":1776809164000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-204226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,19]]},"references-count":28,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jcm-204226","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,19]]}}}