{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T23:01:07Z","timestamp":1773010867642,"version":"3.50.1"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030454388","type":"print"},{"value":"9783030454395","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-45439-5_55","type":"book-chapter","created":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T04:02:50Z","timestamp":1586577770000},"page":"836-851","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction"],"prefix":"10.1007","author":[{"given":"Xianshan","family":"Qu","sequence":"first","affiliation":[]},{"given":"Xiaopeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Csilla","family":"Farkas","sequence":"additional","affiliation":[]},{"given":"John","family":"Rose","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"55_CR1","doi-asserted-by":"crossref","unstructured":"Chen, C., et al.: Multi-domain gated CNN for review helpfulness prediction. In: Proceedings of the 2019 World Wide Web Conference (2019)","DOI":"10.1145\/3308558.3313587"},{"key":"55_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C., Yang, Y., Zhou, J., Li, X., Bao, F.S.: Cross-domain review helpfulness prediction based on convolutional neural networks with auxiliary domain discriminators. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, (Short Papers), vol. 2 (2018)","DOI":"10.18653\/v1\/N18-2095"},{"key":"55_CR3","doi-asserted-by":"crossref","unstructured":"Fan, M., Feng, Y., Sun, M., Li, P., Wang, H., Wang, J.: Multi-task neural learning architecture for end-to-end identification of helpful reviews. In: 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2018)","DOI":"10.1109\/ASONAM.2018.8508623"},{"key":"55_CR4","doi-asserted-by":"crossref","unstructured":"Fan, M., Feng, C., Guo, L., Sun, M., Li, P.: Product-aware helpfulness prediction of online reviews. In: Proceedings of the 2019 World Wide Web Conference (2019)","DOI":"10.1145\/3308558.3313523"},{"key":"55_CR5","doi-asserted-by":"crossref","unstructured":"He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web (2016)","DOI":"10.1145\/2872427.2883037"},{"key":"55_CR6","doi-asserted-by":"crossref","unstructured":"Hong, Y., Lu, J., Yao, J., Zhu, Q., Zhou, G.: What reviews are satisfactory: novel features for automatic helpfulness voting. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval (2012)","DOI":"10.1145\/2348283.2348351"},{"issue":"C","key":"55_CR7","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.chb.2015.01.010","volume":"48","author":"AH Huang","year":"2015","unstructured":"Huang, A.H., Chen, K., Yen, D.C., Tran, T.P.: A study of factors that contribute to online review helpfulness. Comput. Hum. Behav. 48(C), 17\u201327 (2015)","journal-title":"Comput. Hum. Behav."},{"key":"55_CR8","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. In: NAACL HLT 2015, the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2015)","DOI":"10.3115\/v1\/N15-1011"},{"key":"55_CR9","unstructured":"Kats, R.: Surprise! most consumers look at reviews before a purchase (2018). https:\/\/www.emarketer.com\/content\/surprise-most-consumers-look-at-reviews-before-a-purchase. Accessed 20 Aug 2019"},{"key":"55_CR10","doi-asserted-by":"crossref","unstructured":"Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)","DOI":"10.3115\/1610075.1610135"},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"55_CR12","doi-asserted-by":"crossref","unstructured":"Liu, H., et al.: Using argument-based features to predict and analyse review helpfulness. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017)","DOI":"10.18653\/v1\/D17-1142"},{"key":"55_CR13","unstructured":"Liu, J., Cao, Y., Lin, C.Y., Huang, Y., Zhou, M.: Low-quality product review detection in opinion summarization. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) (2007)"},{"key":"55_CR14","doi-asserted-by":"crossref","unstructured":"Lu, Y., Tsaparas, P., Ntoulas, A., Polanyi, L.: Exploiting social context for review quality prediction. In: Proceedings of the 19th International Conference on World Wide Web (2010)","DOI":"10.1145\/1772690.1772761"},{"key":"55_CR15","doi-asserted-by":"crossref","unstructured":"Martin, L., Pu, P.: Prediction of helpful reviews using emotions extraction. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence (2014)","DOI":"10.1609\/aaai.v28i1.8937"},{"key":"55_CR16","doi-asserted-by":"crossref","unstructured":"Melamud, O., Goldberger, J., Dagan, I.: context2vec: learning generic context embedding with bidirectional LSTM. In: CoNLL (2016)","DOI":"10.18653\/v1\/K16-1006"},{"key":"55_CR17","doi-asserted-by":"crossref","unstructured":"Mudambi, S.M., Schuff, D.: What makes a helpful online review? A study of customer reviews on amazon.com. MIS Q. 34(1), 185\u2013200 (2010)","DOI":"10.2307\/20721420"},{"key":"55_CR18","doi-asserted-by":"crossref","unstructured":"Mukherjee, S., Popat, K., Weikum, G.: Exploring latent semantic factors to find useful product reviews. In: Proceedings of the 2017 SIAM International Conference on Data Mining (2017)","DOI":"10.1137\/1.9781611974973.54"},{"key":"55_CR19","doi-asserted-by":"crossref","unstructured":"Ocampo Diaz, G., Ng, V.: Modeling and prediction of online product review helpfulness: a survey. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2018)","DOI":"10.18653\/v1\/P18-1065"},{"key":"55_CR20","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony, M.P., Smyth, B.: Learning to recommend helpful hotel reviews. In: Proceedings of the Third ACM Conference on Recommender Systems (2009)","DOI":"10.1145\/1639714.1639774"},{"key":"55_CR21","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP) (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"55_CR22","doi-asserted-by":"crossref","unstructured":"Qu, X., Li, L., Liu, X., Chen, R., Ge, Y., Choi, S.H.: A dynamic neural network model for CTR prediction in real-time bidding. In: 2019 IEEE International Conference on Big Data (Big Data) (2019)","DOI":"10.1109\/BigData47090.2019.9005598"},{"key":"55_CR23","unstructured":"Qu, X., Li, X., Rose, J.R.: Review helpfulness assessment based on convolutional neural network. arXiv abs\/1808.09016 (2018). http:\/\/arxiv.org\/abs\/1808.09016"},{"key":"55_CR24","doi-asserted-by":"crossref","unstructured":"Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015)","DOI":"10.1145\/2766462.2767830"},{"key":"55_CR25","doi-asserted-by":"crossref","unstructured":"Tang, J., Gao, H., Hu, X., Liu, H.: Context-aware review helpfulness rating prediction. In: Proceedings of the 7th ACM Conference on Recommender Systems (2013)","DOI":"10.1145\/2507157.2507183"},{"key":"55_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Z., Dai, X., Yin, C., Huang, S., Chen, J.: Improving review representations with user attention and product attention for sentiment classification. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.12054"},{"key":"55_CR27","unstructured":"Xiong, W., Litman, D.: Automatically predicting peer-review helpfulness. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2 (2011)"},{"key":"55_CR28","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yan, Y., Qiu, M., Bao, F.: Semantic analysis and helpfulness prediction of text for online product reviews. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (2015)","DOI":"10.3115\/v1\/P15-2007"},{"key":"55_CR29","unstructured":"Yelp: Yelp dataset challenge (2018). https:\/\/www.yelp.com\/dataset\/challenge. Accessed 14 Sept 2018"},{"key":"55_CR30","unstructured":"Zhang, X., LeCun, Y.: Text understanding from scratch. arXiv abs\/1502.01710 (2015)"},{"key":"55_CR31","unstructured":"Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in Neural Information Processing Systems 28 (2015)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45439-5_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:22:23Z","timestamp":1710357743000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45439-5_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454388","9783030454395"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45439-5_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"457","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}