{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T05:47:37Z","timestamp":1784180857581,"version":"3.55.0"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T00:00:00Z","timestamp":1595894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61370137"],"award-info":[{"award-number":["61370137"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key R&D Program of China","award":["2019YFB1406302"],"award-info":[{"award-number":["2019YFB1406302"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s10462-020-09873-y","type":"journal-article","created":{"date-parts":[[2020,7,28]],"date-time":"2020-07-28T07:38:12Z","timestamp":1595921892000},"page":"1171-1200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Review text based rating prediction approaches: preference knowledge learning, representation and utilization"],"prefix":"10.1007","volume":"54","author":[{"given":"James","family":"Chambua","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhendong","family":"Niu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,28]]},"reference":[{"key":"9873_CR1","doi-asserted-by":"publisher","unstructured":"Almahairi A, Kastner K, Cho K, Courville A (2015) Learning distributed representations from reviews for collaborative filtering. In: Proceedings of the 9th ACM conference on recommender systems\u2014RecSys\u201915. ACM, Vienna, pp 147\u2013154. https:\/\/doi.org\/10.1145\/2792838.2800192","DOI":"10.1145\/2792838.2800192"},{"key":"9873_CR2","doi-asserted-by":"publisher","unstructured":"Bansal T, Belanger D, McCallum A (2016) Ask the GRU: multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM conference on recommender systems\u2014RecSys\u201916. ACM, Boston, pp 107\u2013114. https:\/\/doi.org\/10.1145\/2959100.2959180","DOI":"10.1145\/2959100.2959180"},{"issue":"1","key":"9873_CR3","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1093\/imanum\/8.1.141","volume":"8","author":"J Barzilai","year":"1988","unstructured":"Barzilai J, Borwein JM (1988) Two-point step size gradient methods. IMA J Numer Anal 8(1):141\u2013148. https:\/\/doi.org\/10.1093\/imanum\/8.1.141","journal-title":"IMA J Numer Anal"},{"issue":"2","key":"9873_CR4","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1145\/1345448.1345465","volume":"9","author":"RM Bell","year":"2008","unstructured":"Bell RM, Koren Y (2008) Lessons from the Netflix prize challenge. ACM SIGKDD Explor 9(2):75\u201379. https:\/\/doi.org\/10.1145\/1345448.1345465","journal-title":"ACM SIGKDD Explor"},{"issue":"2","key":"9873_CR5","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1145\/1345448.1345459","volume":"9","author":"J Bennett","year":"2007","unstructured":"Bennett J, Tikk D, Liu B, Smyth P, Elkan C (2007) KDD Cup and workshop 2007. ACM SIGKDD Explor 9(2):51\u201352. https:\/\/doi.org\/10.1145\/1345448.1345459","journal-title":"ACM SIGKDD Explor"},{"key":"9873_CR6","doi-asserted-by":"publisher","unstructured":"Blomo J, Ester M, Field M (2013) RecSys challenge 2013. In: 7th ACM conference on recommender systems, RecSys\u201913. Hong Kong, pp 489\u2013490. https:\/\/doi.org\/10.1145\/2507157.2508008","DOI":"10.1145\/2507157.2508008"},{"key":"9873_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BFb0027019","volume-title":"Artificial neural networks: an introduction to ANN theory and practice","author":"PJ Braspenning","year":"1995","unstructured":"Braspenning PJ (1995) Introduction: neural networks as associative devices. In: Braspenning PJ, Thuijsman F, Weijters AJMM (eds) Artificial neural networks: an introduction to ANN theory and practice. Springer, Berlin, pp 1\u20139. https:\/\/doi.org\/10.1007\/BFb0027019"},{"key":"9873_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.aci.2017.09.005","author":"RP Bunker","year":"2017","unstructured":"Bunker RP, Thabtah F (2017) A machine learning framework for sport result prediction. Appl Comput Inf. https:\/\/doi.org\/10.1016\/j.aci.2017.09.005","journal-title":"Appl Comput Inf"},{"issue":"6","key":"9873_CR9","doi-asserted-by":"publisher","first-page":"1487","DOI":"10.3233\/IDA-16320","volume":"21","author":"E \u00c7ano","year":"2017","unstructured":"\u00c7ano E, Morisio M (2017) Hybrid recommender systems: a systematic literature review. Intell Data Anal 21(6):1487\u20131524. https:\/\/doi.org\/10.3233\/IDA-16320","journal-title":"Intell Data Anal"},{"issue":"4","key":"9873_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3017429","volume":"35","author":"D Cao","year":"2017","unstructured":"Cao D, He X, Nie L, Wei X, Hu X, Wu S, Chua T-S (2017) Cross-platform app recommendation by jointly modeling ratings and texts. ACM Trans Inf Syst 35(4):1\u201327. https:\/\/doi.org\/10.1145\/3017429","journal-title":"ACM Trans Inf Syst"},{"key":"9873_CR11","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.neucom.2017.08.040","volume":"275","author":"W Cao","year":"2018","unstructured":"Cao W, Wang X, Ming Z, Gao J (2018) A review on neural networks with random weights. Neurocomputing 275:278\u2013287. https:\/\/doi.org\/10.1016\/j.neucom.2017.08.040","journal-title":"Neurocomputing"},{"key":"9873_CR12","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1016\/j.eswa.2018.07.059","volume":"114","author":"J Chambua","year":"2018","unstructured":"Chambua J, Niu Z, Yousif A, Mbelwa J (2018) Tensor factorization method based on review text semantic similarity for rating prediction. Expert Syst Appl 114:629\u2013638. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.059","journal-title":"Expert Syst Appl"},{"issue":"2","key":"9873_CR13","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1007\/s11257-015-9155-5","volume":"25","author":"L Chen","year":"2015","unstructured":"Chen L, Chen G, Wang F (2015) Recommender systems based on user reviews: the state of the art. User Model User-Adap Inter 25(2):99\u2013154. https:\/\/doi.org\/10.1007\/s11257-015-9155-5","journal-title":"User Model User-Adap Inter"},{"key":"9873_CR14","doi-asserted-by":"publisher","unstructured":"Deng D, Yu J, Jing L, Sun S, Zhou H (2018) Neural gaussian mixture model for review-based rating prediction. In: Proceedings of the 12th ACM conference on recommender systems, RecSys 2018. ACM, Vancouver, BC, pp 113\u2013121. https:\/\/doi.org\/10.1145\/3240323.3240353","DOI":"10.1145\/3240323.3240353"},{"key":"9873_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-018-2098-y","author":"Y Ding","year":"2018","unstructured":"Ding Y, Li S, Yu W, Wang J, Liu M (2018) A unified neural model for review-based rating prediction by leveraging multi-criteria ratings and review text. Cluster Computing. https:\/\/doi.org\/10.1007\/s10586-018-2098-y","journal-title":"Cluster Computing"},{"key":"9873_CR16","doi-asserted-by":"publisher","unstructured":"Dueck D, Morris QD, Frey BJ (2005) Multi-way clustering of microarray data using probabilistic sparse matrix factorization. In: Proceedings 13th international conference on intelligent systems for molecular biology 2005. Detroit, MI, USA, pp 144\u2013151. https:\/\/doi.org\/10.1093\/bioinformatics\/bti1041","DOI":"10.1093\/bioinformatics\/bti1041"},{"key":"9873_CR17","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.asoc.2016.09.049","volume":"52","author":"A Elola","year":"2017","unstructured":"Elola A, Del J, Nekane M, Perfecto C, Alexandre E, Salcedo-sanz S (2017) Hybridizing cartesian genetic programming and harmony search for adaptive feature construction in supervised learning problems. Appl Soft Comput 52:760\u2013770. https:\/\/doi.org\/10.1016\/j.asoc.2016.09.049","journal-title":"Appl Soft Comput"},{"key":"9873_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1201","author":"E Frolov","year":"2017","unstructured":"Frolov E, Oseledets I (2017) Tensor methods and recommender systems. Wiley Interdiscip Rev Data Min Knowl Discov. https:\/\/doi.org\/10.1002\/widm.1201","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"1","key":"9873_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2012.03.001","volume":"38","author":"G Ganu","year":"2012","unstructured":"Ganu G, Kakodkar Y (2012) Improving the quality of predictions using textual information in online user reviews. Inf Syst 38(1):1\u201315. https:\/\/doi.org\/10.1016\/j.is.2012.03.001","journal-title":"Inf Syst"},{"issue":"6","key":"9873_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1002063","volume":"7","author":"AM Hermundstad","year":"2011","unstructured":"Hermundstad AM, Brown KS, Bassett DS, Carlson JM (2011) Learning, memory, and the role of neural network architecture. PLoS Comput Biol 7(6):1\u201314. https:\/\/doi.org\/10.1371\/journal.pcbi.1002063","journal-title":"PLoS Comput Biol"},{"key":"9873_CR21","unstructured":"Hofmann T (1999) Probabilistic latent semantic analysis. In: UAI\u201999: Proceedings of the 15th conference on uncertainty in artificial intelligence. Morgan Kaufmann, Stockholm, pp 289\u2013296. Retrieved from https:\/\/dslpitt.org\/uai\/displayArticleDetails.jsp?mmnu=1%5C&smnu=2%5C&article%5C_id=179%5C&proceeding%5C_id=15"},{"key":"9873_CR22","doi-asserted-by":"publisher","unstructured":"Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, Seattle, pp 168\u2013177. https:\/\/doi.org\/10.1145\/1014052.1014073","DOI":"10.1145\/1014052.1014073"},{"issue":"6","key":"9873_CR23","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1109\/TST.2015.7350016","volume":"20","author":"M Jiang","year":"2015","unstructured":"Jiang M, Song D, Liao L, Zhu F (2015) A bayesian recommender model for user rating and review profiling. Tsinghua Sci Technol 20(6):634\u2013643. https:\/\/doi.org\/10.1109\/TST.2015.7350016","journal-title":"Tsinghua Sci Technol"},{"key":"9873_CR24","doi-asserted-by":"publisher","unstructured":"Jin Z, Li Q, Zeng DD, Zhan Y, Liu R, Wang L, Ma H (2016) Jointly modeling review content and aspect ratings for review rating prediction. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, SIGIR 2016. Pisa, Italy, pp 893\u2013896. https:\/\/doi.org\/10.1145\/2911451.2914692","DOI":"10.1145\/2911451.2914692"},{"key":"9873_CR25","doi-asserted-by":"publisher","unstructured":"Kim D, Park C, Oh J, Lee S, Yu H (2016) Convolutional matrix factorization for document context-aware recommendation. In: Proceedings of the 10th ACM conference on recommender systems RecSys\u201916. ACM, Boston, pp 233\u2013240. https:\/\/doi.org\/10.1145\/2959100.2959165","DOI":"10.1145\/2959100.2959165"},{"key":"9873_CR26","doi-asserted-by":"publisher","unstructured":"Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, Las Vegas, Nevada, USA, August 24\u201327, 2008. ACM, pp 426\u2013434. https:\/\/doi.org\/10.1145\/1401890.1401944","DOI":"10.1145\/1401890.1401944"},{"issue":"8","key":"9873_CR27","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1109\/MC.2009.263","volume":"42","author":"Y Koren","year":"2009","unstructured":"Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. IEEE Comput 42(8):30\u201337. https:\/\/doi.org\/10.1109\/MC.2009.263","journal-title":"IEEE Comput"},{"issue":"4","key":"9873_CR28","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1108\/IMDS-02-2017-0044","volume":"118","author":"H Lee","year":"2018","unstructured":"Lee H, And KC, Yoo D, Suh Y, Lee S, He G (2018) Recommending valuable ideas in an open innovation community overload problem. Ind Manag Data Syst 118(4):683\u2013699. https:\/\/doi.org\/10.1108\/IMDS-02-2017-0044","journal-title":"Ind Manag Data Syst"},{"issue":"9","key":"9873_CR29","doi-asserted-by":"publisher","first-page":"1910","DOI":"10.1109\/TMM.2016.2575738","volume":"18","author":"X Lei","year":"2016","unstructured":"Lei X, Qian X, Zhao G (2016) Rating prediction based on social sentiment from textual reviews. IEEE Trans Multimed 18(9):1910\u20131921. https:\/\/doi.org\/10.1109\/TMM.2016.2575738","journal-title":"IEEE Trans Multimed"},{"key":"9873_CR30","doi-asserted-by":"publisher","unstructured":"Li P, Wang Z, Ren Z, Bing L, Lam W (2017) Neural rating regression with abstractive tips generation for recommendation. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. Shinjuku, Tokyo, Japan, pp 345\u2013354. https:\/\/doi.org\/10.1145\/3077136.3080822","DOI":"10.1145\/3077136.3080822"},{"key":"9873_CR31","doi-asserted-by":"publisher","unstructured":"Ling G, Lyu MR, King I (2014) Ratings meet reviews, a combined approach to recommend. In: Proceedings of the 8th ACM conference on recommender systems\u2014RecSys\u201914. ACM, Foster City, pp 105\u2013112. https:\/\/doi.org\/10.1145\/2645710.2645728","DOI":"10.1145\/2645710.2645728"},{"key":"9873_CR32","doi-asserted-by":"publisher","first-page":"16655","DOI":"10.1109\/ACCESS.2018.2811463","volume":"6","author":"Y Liu","year":"2018","unstructured":"Liu Y, Shen Y (2018) Personal tastes vs. fashion trends: predicting ratings based on visual appearances and reviews. IEEE Access 6:16655\u201316664. https:\/\/doi.org\/10.1109\/ACCESS.2018.2811463","journal-title":"IEEE Access"},{"issue":"1","key":"9873_CR33","doi-asserted-by":"publisher","first-page":"3:1","DOI":"10.1145\/3108238","volume":"12","author":"Y Liu","year":"2017","unstructured":"Liu Y, Liu Y, Shen Y, Li K (2017) Recommendation in a changing world: exploiting temporal dynamics in ratings and reviews. ACM Trans Web 12(1):3:1\u20133:20. https:\/\/doi.org\/10.1145\/3108238","journal-title":"ACM Trans Web"},{"key":"9873_CR34","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.knosys.2011.09.006","volume":"27","author":"X Luo","year":"2012","unstructured":"Luo X, Xia Y, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl Based Syst 27:271\u2013280. https:\/\/doi.org\/10.1016\/j.knosys.2011.09.006","journal-title":"Knowl Based Syst"},{"issue":"1","key":"9873_CR35","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10660-016-9240-9","volume":"17","author":"Y Ma","year":"2017","unstructured":"Ma Y, Chen G, Wei Q (2017) Finding users preferences from large-scale online reviews for personalized recommendation. Electron Commerce Res 17(1):3\u201329. https:\/\/doi.org\/10.1007\/s10660-016-9240-9","journal-title":"Electron Commerce Res"},{"issue":"6","key":"9873_CR36","doi-asserted-by":"publisher","first-page":"6425","DOI":"10.1007\/s11042-017-4550-z","volume":"77","author":"X Ma","year":"2018","unstructured":"Ma X, Lei X, Zhao G, Qian X (2018) Rating prediction by exploring user\u2019s preference and sentiment. Multimed Tools Appl 77(6):6425\u20136444. https:\/\/doi.org\/10.1007\/s11042-017-4550-z","journal-title":"Multimed Tools Appl"},{"key":"9873_CR37","doi-asserted-by":"publisher","unstructured":"Manouselis N, Said A, Drachsler H, Hermanns J, Kille B, Verbert K et al. (2012). Recommender systems challenge 2012. In 6th ACM conference on recommender systems, RecSys\u201912. ACM, Dublin, pp 353\u2013354. https:\/\/doi.org\/10.1145\/2365952.2366043","DOI":"10.1145\/2365952.2366043"},{"key":"9873_CR38","doi-asserted-by":"publisher","unstructured":"McAuley J, Leskovec J (2013) Hidden factors and hidden topics: understanding rating dimensions with review text. In: Proceedings of the 7th ACM conference on recommender systems\u2014RecSys\u201913, pp 165\u2013172. https:\/\/doi.org\/10.1145\/2507157.2507163","DOI":"10.1145\/2507157.2507163"},{"key":"9873_CR39","doi-asserted-by":"publisher","first-page":"8500","DOI":"10.1109\/ACCESS.2016.2633282","volume":"4","author":"Z Miao","year":"2016","unstructured":"Miao Z, Yan J, Chen K, Yang X, Zha H, Zhang W (2016) Joint prediction of rating and popularity for cold-start item by sentinel user selection. IEEE Access 4:8500\u20138513. https:\/\/doi.org\/10.1109\/ACCESS.2016.2633282","journal-title":"IEEE Access"},{"key":"9873_CR40","unstructured":"Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013. Lake Tahoe, pp 3111\u20133119. Retrieved from http:\/\/papers.nips.cc\/paper\/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf"},{"issue":"1","key":"9873_CR41","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/1089815.1089817","volume":"7","author":"RJ Mooney","year":"2007","unstructured":"Mooney RJ, Bunescu R (2007) Mining knowledge from text using information extraction. ACM SIGKDD Explor Newsl 7(1):3\u201310. https:\/\/doi.org\/10.1145\/1089815.1089817","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"9873_CR42","doi-asserted-by":"publisher","unstructured":"Ochi M, Matsuo Y, Okabe M, Onai R (2012) Rating prediction by correcting user rating bias. In: 2012 IEEE\/WIC\/ACM international conference on web intelligence, WI 2012. ACM, Macau, pp 452\u2013456. https:\/\/doi.org\/10.1109\/WI-IAT.2012.186","DOI":"10.1109\/WI-IAT.2012.186"},{"key":"9873_CR43","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.eswa.2017.04.046","volume":"83","author":"TK Paradarami","year":"2017","unstructured":"Paradarami TK, Bastian ND, Wightman JL (2017) A hybrid recommender system using artificial neural networks. Expert Syst Appl 83:300\u2013313. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.046","journal-title":"Expert Syst Appl"},{"key":"9873_CR44","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.datak.2017.06.001","volume":"114","author":"D Pham","year":"2018","unstructured":"Pham D, Le A (2018) Learning multiple layers of knowledge representation for aspect based sentiment analysis. Data Knowl Eng 114:26\u201339. https:\/\/doi.org\/10.1016\/j.datak.2017.06.001","journal-title":"Data Knowl Eng"},{"key":"9873_CR45","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.eswa.2017.12.020","volume":"97","author":"I Portugal","year":"2018","unstructured":"Portugal I, Alencar P, Cowan D (2018) The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst Appl 97:205\u2013227. https:\/\/doi.org\/10.1016\/j.eswa.2017.12.020","journal-title":"Expert Syst Appl"},{"key":"9873_CR46","doi-asserted-by":"publisher","unstructured":"Pradhan L, Zhang C, Bethard S, Chen X (2018) Embedding user behavioral aspect in TF-IDF like representation. In: IEEE 1st Conference on multimedia information processing and retrieval, MIPR 2018. IEEE, Miami, pp 262\u2013267. https:\/\/doi.org\/10.1109\/MIPR.2018.00061","DOI":"10.1109\/MIPR.2018.00061"},{"key":"9873_CR47","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.knosys.2016.07.033","volume":"110","author":"L Qiu","year":"2016","unstructured":"Qiu L, Gao S, Cheng W, Guo J (2016) Aspect-based latent factor model by integrating ratings and reviews for recommender system. Knowl Based Syst 110:233\u2013243. https:\/\/doi.org\/10.1016\/j.knosys.2016.07.033","journal-title":"Knowl Based Syst"},{"key":"9873_CR48","doi-asserted-by":"publisher","unstructured":"Said A, Dooms S, Loni B, Tikk D (2014) Recommender systems challenge 2014. In: 8th ACM conference on recommender systems, RecSys\u201914. ACM, Foster City, Silicon Valley, pp 387\u2013388. https:\/\/doi.org\/10.1145\/2645710.2645779","DOI":"10.1145\/2645710.2645779"},{"key":"9873_CR49","doi-asserted-by":"publisher","unstructured":"Salakhutdinov R, Mnih A (2007) Probabilistic matrix factorization. In: Proceedings of advances in neural information processing systems 20 (NIPS 07), pp 1257\u20131264. https:\/\/doi.org\/10.1145\/1390156.1390267","DOI":"10.1145\/1390156.1390267"},{"issue":"4","key":"9873_CR50","doi-asserted-by":"publisher","first-page":"1393","DOI":"10.1016\/j.eswa.2012.08.049","volume":"40","author":"D S\u00e1nchez","year":"2013","unstructured":"S\u00e1nchez D, Batet M (2013) A semantic similarity method based on information content exploiting multiple ontologies. Expert Syst Appl 40(4):1393\u20131399. https:\/\/doi.org\/10.1016\/j.eswa.2012.08.049","journal-title":"Expert Syst Appl"},{"key":"9873_CR51","doi-asserted-by":"publisher","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM conference on electronic commerce (EC-00). ACM, Minneapolis, pp 158\u2013167. https:\/\/doi.org\/10.1145\/352871.352887","DOI":"10.1145\/352871.352887"},{"key":"9873_CR52","doi-asserted-by":"publisher","unstructured":"Seo S, Huang J, Yang H, Liu Y (2017) Interpretable convolutional neural networks with dual local and global attention for review rating prediction. In: Proceedings of the 11th ACM conference on recommender systems\u2014RecSys\u201917. Como, Italy, pp 297\u2013305. https:\/\/doi.org\/10.1145\/3109859.3109890","DOI":"10.1145\/3109859.3109890"},{"issue":"9","key":"9873_CR53","doi-asserted-by":"publisher","first-page":"424","DOI":"10.3390\/e19080424","volume":"19","author":"J Shi","year":"2017","unstructured":"Shi J, Zheng X, Wei Y (2017) Survey on probabilistic models of low-rank matrix factorizations. Entropy 19(9):424\u2013457. https:\/\/doi.org\/10.3390\/e19080424","journal-title":"Entropy"},{"key":"9873_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2009\/421425","volume":"2009","author":"X Su","year":"2009","unstructured":"Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1\u201319. https:\/\/doi.org\/10.1155\/2009\/421425","journal-title":"Adv Artif Intell"},{"issue":"1","key":"9873_CR55","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10462-017-9539-5","volume":"50","author":"JK Tarus","year":"2018","unstructured":"Tarus JK, Niu Z, Mustafa G (2018) Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning. Artif Intell Rev 50(1):21\u201348. https:\/\/doi.org\/10.1007\/s10462-017-9539-5","journal-title":"Artif Intell Rev"},{"key":"9873_CR56","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.knosys.2017.11.003","volume":"140","author":"NM Villegas","year":"2018","unstructured":"Villegas NM, S\u00e1nchez C, D\u00edaz-cely J, Tamura G (2018) Characterizing context-aware recommender systems: a systematic literature review. Knowl Based Syst 140:173\u2013200. https:\/\/doi.org\/10.1016\/j.knosys.2017.11.003","journal-title":"Knowl Based Syst"},{"issue":"5","key":"9873_CR57","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/TKDE.2019.2895033","volume":"32","author":"S Wan","year":"2020","unstructured":"Wan S, Niu Z (2020) A hybrid E-learning recommendation approach based on learners\u2019 influence propagation. IEEE Trans Knowl Data Eng 32(5):827\u2013840","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9873_CR58","doi-asserted-by":"publisher","unstructured":"Wang C, Blei DM (2011) Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining\u2014KDD\u201911. ACM, San Diego, pp 448\u2013456. https:\/\/doi.org\/10.1145\/2020408.2020480","DOI":"10.1145\/2020408.2020480"},{"key":"9873_CR59","doi-asserted-by":"publisher","unstructured":"Wang Y, Liu Y, Yu X (2012) Collaborative filtering with aspect-based opinion mining: a tensor factorization approach. In: Proceedings\u2014IEEE international conference on data mining, ICDM, pp 1152\u20131157. https:\/\/doi.org\/10.1109\/ICDM.2012.76","DOI":"10.1109\/ICDM.2012.76"},{"key":"9873_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/WEB-180370","volume":"16","author":"J Wang","year":"2018","unstructured":"Wang J, Huang J, Zhong N (2018a) Exploiting item\u2013item relations to improve review-based rating prediction. Web Intell 16:1\u201313. https:\/\/doi.org\/10.3233\/WEB-180370","journal-title":"Web Intell"},{"issue":"9","key":"9873_CR61","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1587\/transinf.2017EDP7180","volume":"101","author":"Y Wang","year":"2018","unstructured":"Wang Y, Zhong Z, Yang A, Jing N (2018b) Review rating prediction on location-based social networks using text, social links, and geolocations. IEICE Trans 101(9):2298\u20132306. https:\/\/doi.org\/10.1587\/transinf.2017EDP7180","journal-title":"IEICE Trans"},{"key":"9873_CR62","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.knosys.2018.01.003","volume":"145","author":"H Wu","year":"2018","unstructured":"Wu H, Zhang Z, Yue K, Zhang B, He J, Sun L (2018) Dual-regularized matrix factorization with deep neural networks for recommender systems. Knowl Based Syst 145:46\u201358. https:\/\/doi.org\/10.1016\/j.knosys.2018.01.003","journal-title":"Knowl Based Syst"},{"key":"9873_CR63","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.engappai.2015.07.012","volume":"45","author":"Yueshen Xu","year":"2015","unstructured":"Xu Yueshen, Yin J (2015) Collaborative recommendation with user generated content. Eng Appl Artif Intell 45:281\u2013294. https:\/\/doi.org\/10.1016\/j.engappai.2015.07.012","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"9873_CR64","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/MIS.2016.4","volume":"31","author":"G Xu","year":"2016","unstructured":"Xu G, Fu B, Gu Y (2016) Point-of-interest recommendations via a supervised random walk algorithm. IEEE Intell Syst 31(1):15\u201323. https:\/\/doi.org\/10.1109\/MIS.2016.4","journal-title":"IEEE Intell Syst"},{"issue":"1","key":"9873_CR65","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s10115-016-1005-1","volume":"52","author":"Yinqing Xu","year":"2017","unstructured":"Xu Yinqing, Yu Q, Lam W, Lin T (2017) Exploiting interactions of review text, hidden user communities and item groups, and time for collaborative filtering. Knowl Inf Syst 52(1):221\u2013254. https:\/\/doi.org\/10.1007\/s10115-016-1005-1","journal-title":"Knowl Inf Syst"},{"key":"9873_CR66","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2015.12.136","volume":"210","author":"C Yang","year":"2016","unstructured":"Yang C, Yu X, Liu Y, Nie Y, Wang Y (2016) Collaborative filtering with weighted opinion aspects. Neurocomputing 210:185\u2013196. https:\/\/doi.org\/10.1016\/j.neucom.2015.12.136","journal-title":"Neurocomputing"},{"key":"9873_CR67","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.ipl.2016.08.002","volume":"117","author":"D Yu","year":"2017","unstructured":"Yu D, Mu Y, Jin Y (2017) Rating prediction using review texts with underlying sentiments. Inf Process Lett 117:10\u201318. https:\/\/doi.org\/10.1016\/j.ipl.2016.08.002","journal-title":"Inf Process Lett"},{"key":"9873_CR68","doi-asserted-by":"publisher","first-page":"54106","DOI":"10.1109\/ACCESS.2018.2871970","volume":"6","author":"JD Zhang","year":"2018","unstructured":"Zhang JD, Chow CY (2018) SEMA: deeply learning semantic meanings and temporal dynamics for recommendations. IEEE Access 6:54106\u201354116. https:\/\/doi.org\/10.1109\/ACCESS.2018.2871970","journal-title":"IEEE Access"},{"issue":"11","key":"9873_CR69","doi-asserted-by":"publisher","first-page":"3013","DOI":"10.1109\/TKDE.2016.2598740","volume":"28","author":"W Zhang","year":"2016","unstructured":"Zhang W, Wang J (2016) Integrating topic and latent factors for scalable personalized review-based rating prediction. IEEE Trans Knowl Data Eng 28(11):3013\u20133027. https:\/\/doi.org\/10.1109\/TKDE.2016.2598740","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9873_CR70","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.neucom.2012.05.010","volume":"97","author":"Z Zhang","year":"2012","unstructured":"Zhang Z, Zhao K, Zha H (2012) Inducible regularization for low-rank matrix factorizations for collaborative filtering. Neurocomputing 97:52\u201362. https:\/\/doi.org\/10.1016\/j.neucom.2012.05.010","journal-title":"Neurocomputing"},{"key":"9873_CR71","doi-asserted-by":"publisher","unstructured":"Zhang Y, Lai G, Zhang M, Zhang Y, Liu Y, Ma S (2014) 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\u2014SIGIR\u201914. ACM, Gold Coast, pp 83\u201392. https:\/\/doi.org\/10.1145\/2600428.2609579","DOI":"10.1145\/2600428.2609579"},{"key":"9873_CR72","doi-asserted-by":"publisher","unstructured":"Zhang Y, Ai Q, Chen X, Croft WB (2017) Joint representation learning for top-n recommendation with heterogeneous information sources. In: Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017. Singapore, pp 1449\u20131458. https:\/\/doi.org\/10.1145\/3132847.3132892","DOI":"10.1145\/3132847.3132892"},{"key":"9873_CR73","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.ins.2016.08.042","volume":"372","author":"X Zheng","year":"2016","unstructured":"Zheng X, Ding W, Lin Z, Chen C (2016) Topic tensor factorization for recommender system. Inf Sci 372:276\u2013293. https:\/\/doi.org\/10.1016\/j.ins.2016.08.042","journal-title":"Inf Sci"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09873-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-020-09873-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-020-09873-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T23:16:08Z","timestamp":1627427768000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-020-09873-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,28]]},"references-count":73,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["9873"],"URL":"https:\/\/doi.org\/10.1007\/s10462-020-09873-y","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,28]]},"assertion":[{"value":"28 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}