{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:36:45Z","timestamp":1777696605631,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2024,4,1]]},"abstract":"<jats:p>Recently, most studies in the field have focused on integrating reviews behind ratings to improve recommendation performance. However, two main problems remain (1) Most works use a unified data form and the same processing method to address the user and the item reviews, regardless of their essential differences. (2) Most works only adopt simple concatenation operation when constructing user-item interaction, thus ignoring the multilevel relationship between the user and the item, which may lead to suboptimal recommendation performance. In this paper, we propose a novel Asymmetric Multi-Level Interactive Attention Network (AMLIAN) integrating reviews for item recommendation. AMLIAN can predict precise ratings to help the user make better and faster decisions. Specifically, to address the essential difference between the user and the item reviews, AMLIAN uses the asymmetric network to construct user and item features using different data forms (document-level and review-level). To learn more personalized user-item interaction, the user ID and item ID and some processed features of user reviews and item reviews are respectively used for multilevel relationships. Experiments on five real-world datasets show that AMLIAN significantly outperforms state-of-the-art methods.<\/jats:p>","DOI":"10.3233\/ida-230128","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T11:16:48Z","timestamp":1688123808000},"page":"433-450","source":"Crossref","is-referenced-by-count":2,"title":["Asymmetric multilevel interactive attention network integrating reviews for item recommendation"],"prefix":"10.1177","volume":"28","author":[{"given":"Peilin","family":"Yang","sequence":"first","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Tianjin University of Technology, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenguang","family":"Zheng","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Tianjin University of Technology, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingyuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Learning-Based Intelligent System, Tianjin University of Technology, Tianjin, China"},{"name":"Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xu","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of General Education, Tianjin Foreign Studies University, Tianjin, China"},{"name":"Department of Computer Science, Norwegian University of Science and Technology, Gj\u00f8vik, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"11","key":"10.3233\/IDA-230128_ref2","doi-asserted-by":"crossref","first-page":"6726","DOI":"10.1109\/TNNLS.2021.3083264","article-title":"Deep rating and review neural network for item recommendation","volume":"33","author":"Xi","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"8","key":"10.3233\/IDA-230128_ref4","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"issue":"5","key":"10.3233\/IDA-230128_ref5","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.1007\/s10489-021-02666-y","article-title":"A multi-task dual attention deep recommendation model using ratings and review helpfulness","volume":"52","author":"Liu","year":"2022","journal-title":"Applied Intelligence"},{"issue":"5","key":"10.3233\/IDA-230128_ref6","first-page":"1906","article-title":"Deep variational matrix factorization with knowledge embedding for recommendation system","volume":"33","author":"Shen","year":"2019","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"9","key":"10.3233\/IDA-230128_ref7","doi-asserted-by":"crossref","first-page":"6124","DOI":"10.1109\/TII.2019.2958696","article-title":"Trust-enhanced collaborative filtering for personalized point of interests recommendation","volume":"16","author":"Wang","year":"2019","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.3233\/IDA-230128_ref10","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.neucom.2021.03.122","article-title":"CARM: Confidence-aware recommender model via review representation learning and historical rating behavior in the online platforms","volume":"455","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"issue":"3","key":"10.3233\/IDA-230128_ref11","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.1109\/TNNLS.2020.2984665","article-title":"Came: Content-and context-aware music embedding for recommendation","volume":"32","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"3","key":"10.3233\/IDA-230128_ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3309546","article-title":"Attentive aspect modeling for review-aware recommendation","volume":"37","author":"Guan","year":"2019","journal-title":"TOIS"},{"key":"10.3233\/IDA-230128_ref16","doi-asserted-by":"crossref","unstructured":"X. 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Wang, Lightgcn: Simplifying and powering graph convolution network for recommendation, in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 639\u2013648.","DOI":"10.1145\/3397271.3401063"},{"key":"10.3233\/IDA-230128_ref17","doi-asserted-by":"crossref","first-page":"107777","DOI":"10.1016\/j.asoc.2021.107777","article-title":"A feature interaction learning approach for crowdfunding project recommendation","volume":"112","author":"Xiao","year":"2021","journal-title":"Applied Soft Computing"},{"issue":"6","key":"10.3233\/IDA-230128_ref18","doi-asserted-by":"crossref","first-page":"6432","DOI":"10.1007\/s10489-022-03748-1","article-title":"Siga: Social influence modeling integrating graph autoencoder for rating prediction","volume":"53","author":"Liu","year":"2023","journal-title":"Applied Intelligence"},{"issue":"2","key":"10.3233\/IDA-230128_ref24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298988","article-title":"A context-aware user-item representation learning for item recommendation","volume":"37","author":"Wu","year":"2019","journal-title":"TOIS"},{"key":"10.3233\/IDA-230128_ref26","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ins.2021.03.034","article-title":"TAERT: Triple-attentional explainable recommendation with temporal convolutional network","volume":"567","author":"Guo","year":"2021","journal-title":"Information Sciences"},{"issue":"7","key":"10.3233\/IDA-230128_ref27","doi-asserted-by":"crossref","first-page":"4361","DOI":"10.1109\/TII.2021.3128240","article-title":"EDMF: Efficient deep matrix factorization with review feature learning for industrial recommender system","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"10.3233\/IDA-230128_ref29","unstructured":"A. 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