{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T19:49:22Z","timestamp":1766087362013,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,20]],"date-time":"2018-12-20T00:00:00Z","timestamp":1545264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Foundation of He\u2019nan Educational Committee","award":["19A520032"],"award-info":[{"award-number":["19A520032"]}]},{"name":"Ph.D. Start-up Foundation of Pingdingshan University","award":["PXY-BSQD-2018007"],"award-info":[{"award-number":["PXY-BSQD-2018007"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1536201, U1405254, and 61271392"],"award-info":[{"award-number":["U1536201, U1405254, and 61271392"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user\u2019s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/info10010001","type":"journal-article","created":{"date-parts":[[2018,12,20]],"date-time":"2018-12-20T12:54:36Z","timestamp":1545310476000},"page":"1","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix"],"prefix":"10.3390","volume":"10","author":[{"given":"Bingkun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer, Pingdingshan University, Pingdingshan 467000, China"}]},{"given":"Bing","family":"Chen","sequence":"additional","affiliation":[{"name":"Huanghe Science &amp; Technology University, Zhengzhou 450000, China"}]},{"given":"Li","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer, Pingdingshan University, Pingdingshan 467000, China"}]},{"given":"Gaiyun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer, Pingdingshan University, Pingdingshan 467000, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.ipm.2016.07.001","article-title":"Analytical mapping of opinion mining and sentiment analysis research during 2000\u20132015","volume":"53","author":"Piryani","year":"2016","journal-title":"Inf. Process. Manag."},{"key":"ref_2","first-page":"1","article-title":"Sentiment analysis and opinion mining","volume":"5","author":"Liu","year":"2012","journal-title":"Synth. Lect. Hum. Lang. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1007\/s10115-016-0993-1","article-title":"A semi-supervised approach to sentiment analysis using revised sentiment strength based on Senti Word Net","volume":"51","author":"Khan","year":"2017","journal-title":"Knowl. Inf. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.ins.2016.07.028","article-title":"e SAP: A decision support framework for enhanced sentiment analysis and polarity classification","volume":"367","author":"Khan","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1007\/s12559-016-9386-8","article-title":"Multi-objective model selection (MOMS)-based semi-supervised framework for sentiment analysis","volume":"8","author":"Khan","year":"2016","journal-title":"Cognit. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.knosys.2016.02.011","article-title":"SWIMS: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis","volume":"100","author":"Khan","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1613\/jair.4272","article-title":"Sentiment analysis of short informal texts","volume":"50","author":"Kiritchenko","year":"2014","journal-title":"J. Artif. Intell. Res."},{"unstructured":"Horrigan, J. \u201cOnline shopping,\u201d Pew Internet and American Life Project Report. Available online: http:\/\/www.pewinternet.org\/2008\/02\/13\/online-shopping\/.","key":"ref_8"},{"doi-asserted-by":"crossref","unstructured":"Wu, Y., and Ester, M. (2015, January 2\u20136). FLAME: A probabilistic model combining aspect based opinion mining and collaborative filtering. Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, Shanghai, China.","key":"ref_9","DOI":"10.1145\/2684822.2685291"},{"unstructured":"Qu, L., Ifrim, G., and Weikum, G. (2010, January 23\u201327). The bag-of-opinions method for review rating prediction from sparse text patterns. Proceedings of the 23rd International Conference on Computational Linguistics, Beijing, China.","key":"ref_10"},{"unstructured":"Li, F., Liu, N., Jin, H., Zhao, K., Yang, Q., and Zhu, X. (2011, January 16\u201322). Incorporating reviewer and item information for review rating prediction. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain.","key":"ref_11"},{"unstructured":"Ganu, G., Elhadad, N., and Marian, A. (2009, January 28). Beyond the Stars: Improving Rating Predictions using Review Text Content. Proceedings of the Twelfth International Workshop on the Web and Databases, WebDB, Providence, RI, USA.","key":"ref_12"},{"doi-asserted-by":"crossref","unstructured":"Zheng, L., Noroozi, V., and Yu, P.S. (2017, January 6\u201310). Joint deep modeling of users and items using reviews for recommendation. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK.","key":"ref_13","DOI":"10.1145\/3018661.3018665"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Lu, Y., and Zhai, C. (2010, January 24\u201328). Latent aspect rating analysis on review text data: A rating regression approach. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","key":"ref_14","DOI":"10.1145\/1835804.1835903"},{"doi-asserted-by":"crossref","unstructured":"Wu, F., and Huang, Y. (2016, January 12\u201317). Personalized Microblog Sentiment Classification via Multi-Task Learning. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Phoenix, AZ, USA.","key":"ref_15","DOI":"10.1609\/aaai.v30i1.10378"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/2556270","article-title":"Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges","volume":"47","author":"Shi","year":"2014","journal-title":"ACM Comput. Surv."},{"unstructured":"Ma, H. (August, January 28). An experimental study on implicit social recommendation. Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland.","key":"ref_17"},{"key":"ref_18","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"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1145\/1721654.1721677","article-title":"Collaborative filtering with temporal dynamics","volume":"53","author":"Koren","year":"2010","journal-title":"Commun. ACM"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1016\/j.chb.2014.12.011","article-title":"A collaborative user-centered framework for recommending items in Online Social Networks","volume":"51","author":"Colace","year":"2015","journal-title":"Comput. Hum. Behav."},{"doi-asserted-by":"crossref","unstructured":"Yu, K., Zhu, S., Lafferty, J., and Gong, Y. (2009, January 19\u201323). Fast nonparametric matrix factorization for large-scale collaborative filtering. Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Boston, MA, USA.","key":"ref_21","DOI":"10.1145\/1571941.1571979"},{"doi-asserted-by":"crossref","unstructured":"Li, P., Wang, Z., Ren, Z., Bing, L., and Lam, W. (2017, January 07\u201311). Neural rating regression with abstractive tips generation for recommendation. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","key":"ref_22","DOI":"10.1145\/3077136.3080822"},{"doi-asserted-by":"crossref","unstructured":"Pang, B., and Lee, L. (2005, January 25\u201330). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, Ann Arbor, MI, USA.","key":"ref_23","DOI":"10.3115\/1219840.1219855"},{"doi-asserted-by":"crossref","unstructured":"Liu, J., and Seneff, S. (2009, January 6\u20137). Review sentiment scoring via a parse-and-paraphrase paradigm. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, Singapore.","key":"ref_24","DOI":"10.3115\/1699510.1699532"},{"doi-asserted-by":"crossref","unstructured":"Lee, H.C., Lee, S.J., and Chung, Y.J. (2007, January 20\u201322). A study on the improved collaborative filtering algorithm for recommender system. Proceedings of the 5th ACIS International Conference on Software Engineering Research, Management & Applications (SERA 2007), Busan, Korea.","key":"ref_25","DOI":"10.1109\/SERA.2007.33"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.ins.2009.10.016","article-title":"Improving memory-based collaborative filtering via similarity updating and prediction modulation","volume":"180","author":"Jeong","year":"2010","journal-title":"Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Covington, P., Adams, J., and Sargin, E. (2016, January 15\u201319). Deep neural networks for youtube recommendations. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.","key":"ref_27","DOI":"10.1145\/2959100.2959190"},{"doi-asserted-by":"crossref","unstructured":"He, X., and Chua, T.S. (2017, January 7\u201311). Neural factorization machines for sparse predictive analytics. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, Tokyo, Japan.","key":"ref_28","DOI":"10.1145\/3077136.3080777"},{"doi-asserted-by":"crossref","unstructured":"Catherine, R., and Cohen, W. (arXiv, 2017). TransNets: Learning to Transform for Recommendation, arXiv.","key":"ref_29","DOI":"10.1145\/3109859.3109878"},{"doi-asserted-by":"crossref","unstructured":"Kim, D., Park, C., Oh, J., Lee, S., and Yu, H. (2016, January 15\u201319). Convolutional Matrix Factorization for Document Context-Aware Recommendation. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA.","key":"ref_30","DOI":"10.1145\/2959100.2959165"},{"doi-asserted-by":"crossref","unstructured":"Seo, S., Huang, J., Yang, H., and Liu, Y. (2017, January 27\u201331). Interpretable convolutional neural networks with dual local and global attention for review rating prediction. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, Italy.","key":"ref_31","DOI":"10.1145\/3109859.3109890"},{"doi-asserted-by":"crossref","unstructured":"He, X., Chen, T., Kan, M.Y., and Chen, X. (2015, January 18\u201323). Trirank: Review-aware explainable recommendation by modeling aspects. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia.","key":"ref_32","DOI":"10.1145\/2806416.2806504"},{"doi-asserted-by":"crossref","unstructured":"Ling, G., Lyu, M.R., and King, I. (2014, January 6\u201310). Ratings meet reviews, a combined approach to recommend. Proceedings of the 8th ACM Conference on Recommender systems, Foster City, CA, USA.","key":"ref_33","DOI":"10.1145\/2645710.2645728"},{"doi-asserted-by":"crossref","unstructured":"McAuley, J., and Leskovec, J. (2013, January 12\u201316). Hidden factors and hidden topics: Understanding rating dimensions with review text. Proceedings of the 7th ACM Conference on Recommender Systems, Hong Kong, China.","key":"ref_34","DOI":"10.1145\/2507157.2507163"},{"doi-asserted-by":"crossref","unstructured":"Ren, Z., Liang, S., Li, P., Wang, S., and de Rijke, M. (2017, January 6\u201310). Social collaborative viewpoint regression with explainable recommendations. Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK.","key":"ref_35","DOI":"10.1145\/3018661.3018686"},{"doi-asserted-by":"crossref","unstructured":"Bao, Y., Fang, H., and Zhang, J. (2014, January 27\u201331). Topicmf: Simultaneously exploiting ratings and reviews for recommendation. Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, QC, Canada.","key":"ref_36","DOI":"10.1609\/aaai.v28i1.8715"},{"doi-asserted-by":"crossref","unstructured":"Diao, Q., Qiu, M., Wu, C., Smola, A.J., Jiang, J., and Wang, C. (2014, January 24\u201327). Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","key":"ref_37","DOI":"10.1145\/2623330.2623758"},{"doi-asserted-by":"crossref","unstructured":"Jakob, N., Weber, S.H., M\u00fcller, M.C., and Gurevych, I. (2009, January 6). Beyond the stars: Exploiting free-text user reviews to improve the accuracy of movie recommendations. Proceedings of the 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, Hong Kong, China.","key":"ref_38","DOI":"10.1145\/1651461.1651473"},{"unstructured":"Zhang, W., Yuan, Q., Han, J., and Wang, J. (2016, January 9\u201315). Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA.","key":"ref_39"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ai, Q., Chen, X., and Croft, W.B. (2017, January 6\u201310). Joint representation learning for top-n recommendation with heterogeneous information sources. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore.","key":"ref_40","DOI":"10.1145\/3132847.3132892"},{"doi-asserted-by":"crossref","unstructured":"Gong, L., Al Boni, M., and Wang, H. (2016, January 7\u201312). Modeling social norms evolution for personalized sentiment classification. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, Germany.","key":"ref_41","DOI":"10.18653\/v1\/P16-1081"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/1\/1\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:35:13Z","timestamp":1760196913000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/1\/1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,20]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["info10010001"],"URL":"https:\/\/doi.org\/10.3390\/info10010001","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2018,12,20]]}}}