{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,10]],"date-time":"2024-08-10T05:58:28Z","timestamp":1723269508040},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1016\/j.neucom.2020.10.041","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T02:47:16Z","timestamp":1603334836000},"page":"235-247","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["Attentive preference personalized recommendation with sentence-level explanations"],"prefix":"10.1016","volume":"426","author":[{"given":"Jin","family":"Xie","sequence":"first","affiliation":[]},{"given":"Fuxi","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Xuefei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Sheng","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Shichao","family":"Liu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.neucom.2020.10.041_b0005","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1145\/1345448.1345465","article-title":"Lessons from the Netflix prize chanllenge","volume":"9","author":"Bell","year":"2007","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"10.1016\/j.neucom.2020.10.041_b0010","doi-asserted-by":"crossref","first-page":"8(2009)","DOI":"10.1109\/MC.2009.263","article-title":"Matrix Factorization techniques for recommender systems","volume":"42","author":"Koren","year":"2009","journal-title":"Computer"},{"key":"10.1016\/j.neucom.2020.10.041_b0015","series-title":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","first-page":"485","article-title":"Social collaborative viewpoint regression with explainable recommendations","author":"Ren","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0020","series-title":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","first-page":"1661","article-title":"Trirank: review-aware explainable recommendation by modeling aspects","author":"He","year":"2015"},{"key":"10.1016\/j.neucom.2020.10.041_b0025","series-title":"Proceedings of the 8th ACM Conference on Recommender Systems","first-page":"105","article-title":"Ratings meet review, a combined approach to recommend","author":"Ling","year":"2014"},{"key":"10.1016\/j.neucom.2020.10.041_b0030","series-title":"In Proceedings of the 25th International Joint Conference on Artificial Intelligence","article-title":"Rating-boosted latent topics: Understanding users and items with ratings and reviews","author":"Tan","year":"2016"},{"key":"10.1016\/j.neucom.2020.10.041_b0035","series-title":"Proceedings of the 8th ACM International Conference on Web Search and Data Mining","first-page":"199","article-title":"FLAME: A probabilistic model combining aspect based opinion mining and collaborative filtering","author":"Yao","year":"2015"},{"issue":"11","key":"10.1016\/j.neucom.2020.10.041_b0040","doi-asserted-by":"crossref","first-page":"3013","DOI":"10.1109\/TKDE.2016.2598740","article-title":"Integrating topic and latent factors for scalable personalized review-based rating prediction","volume":"28","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"2","key":"10.1016\/j.neucom.2020.10.041_b0045","doi-asserted-by":"crossref","DOI":"10.1145\/3291060","article-title":"Explainable recommendation by leveraging reviews and images","volume":"37","author":"Cheng","year":"2019","journal-title":"ACM Trans. Inf. Syst."},{"key":"10.1016\/j.neucom.2020.10.041_b0050","series-title":"RecSys\u201918","article-title":"Explore, exploit, and explain: personalizing explainable recommendations with bandits","author":"Mcinerney","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.041_b0055","series-title":"Proceedings of the 11th ACM Conference on Recommender Systems","first-page":"288","article-title":"TransNets: learning to transform for recommendation","author":"Catherine","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0060","unstructured":"Donghyun Kim, Chanyoung Park, Jinoh Oh, Sungyoung Lee, and Hwanjo Yu. Convolutional Matrix Factorization for Document Context-Aware Recommendation, in Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16), 2016, pp 233\u2013240."},{"issue":"2","key":"10.1016\/j.neucom.2020.10.041_b0070","article-title":"Unsupervised learning of paragraph embeddings for context-aware recommendation","volume":"7","author":"Xie","year":"2019","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.neucom.2020.10.041_b0075","first-page":"22:1","article-title":"A context-aware user-item representation learning for item recommendation","volume":"37","author":"Libing","year":"2019","journal-title":"ACM Trans. Inf. Syst"},{"key":"10.1016\/j.neucom.2020.10.041_b0080","doi-asserted-by":"crossref","unstructured":"C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201911, 2011, pp 448\u2013456.","DOI":"10.1145\/2020408.2020480"},{"key":"10.1016\/j.neucom.2020.10.041_b0085","series-title":"Proceedings of the 7th ACM Conference on Recommender Systems","first-page":"165","article-title":"Hidden factors and hidden topics: understanding rating dimensions with review text","author":"McAuley","year":"2013"},{"key":"10.1016\/j.neucom.2020.10.041_b0090","series-title":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence","first-page":"2","article-title":"TopicMF: Simultaneously exploiting ratings and reviews for recommendation","author":"Bao","year":"2014"},{"key":"10.1016\/j.neucom.2020.10.041_b0095","series-title":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","first-page":"2640","article-title":"Rating-boosted latent topics: understanding users and items with ratings and reviews","author":"Tan","year":"2016"},{"key":"10.1016\/j.neucom.2020.10.041_b0100","series-title":"Proceeding of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)","first-page":"1309","article-title":"A hybrid collaborative filtering model with deep structure for recommender systems","author":"Dong","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0105","series-title":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining (WSDM '17)","first-page":"425","article-title":"Joint deep modeling of users and items using reviews for recommendation","author":"Zheng","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0110","doi-asserted-by":"crossref","unstructured":"Matteo Pagliardini, Prakhar Gupta, Martin Jaggi. Unsupervised learning of sentence embeddings using compositional n-gram features, in: The North American Chapter of the Association for Computational Linguistics (NAACL 2018), 2018.","DOI":"10.18653\/v1\/N18-1049"},{"key":"10.1016\/j.neucom.2020.10.041_b0115","first-page":"135","article-title":"Enriching word vectors with subword information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Ling. (ACL)"},{"key":"10.1016\/j.neucom.2020.10.041_b0120","series-title":"Proceedings of the 2018 World Wide Web Conference on World Wide Web","first-page":"1583","article-title":"Neural attentional rating regression with review-level explanations","author":"Chen","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.041_b0125","series-title":"Proceedings of the conference on world wide web","first-page":"773","article-title":"Coevolutionary recommendation model: mutual learning between ratings and reviews","author":"Yichao","year":"2018"},{"key":"10.1016\/j.neucom.2020.10.041_b0130","series-title":"RecSys \u201917: Proceedings of the Eleventh ACM Conference on Recommender Systems","first-page":"297","article-title":"Interpretable Convolutional Neural Network with dual local and global attention for review rating prediction","author":"Seo","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0135","unstructured":"R. Salakhutdinov, A. Mnih, Probabilistic Matrix Factorization, Advances in Neural Information Processing Systems 2007 (NIPS\u201907), 2008, pp. 1257\u20131264."},{"key":"10.1016\/j.neucom.2020.10.041_b0145","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing","first-page":"1746","article-title":"Convolutional neural networks for sentence classification","author":"Kim","year":"2014"},{"key":"10.1016\/j.neucom.2020.10.041_b0150","series-title":"Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics","first-page":"1722","article-title":"Context-aware network embedding for relation modeling","author":"Cunchao","year":"2017"},{"key":"10.1016\/j.neucom.2020.10.041_b0155","series-title":"Proceedings of the 25th International Conference on World Wide Web","first-page":"507","article-title":"Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2020.10.041_b0160","series-title":"SIGIR","first-page":"83","article-title":"Explicit factor models for explainable recommendation based on phrase-level sentiment analysis","author":"Zhang","year":"2014"},{"key":"10.1016\/j.neucom.2020.10.041_b0165","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neucom.2019.09.052","article-title":"Hybrid neural recommendation with joint deep representation learning of ratings and reviews","volume":"374","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neucom.2020.10.041_b0170","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/TII.2015.2475218","article-title":"A triple wing harmonium model for movie recommendation","volume":"12","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Ind. Inf."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220315903?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231220315903?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T09:00:52Z","timestamp":1696928452000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231220315903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":32,"alternative-id":["S0925231220315903"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.041","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2021,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Attentive preference personalized recommendation with sentence-level explanations","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2020.10.041","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}
