{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T12:28:27Z","timestamp":1759580907291,"version":"3.40.3"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030997380"},{"type":"electronic","value":"9783030997397"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-99739-7_27","type":"book-chapter","created":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T23:02:47Z","timestamp":1649113367000},"page":"231-239","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["What Matters for Shoppers: Investigating Key Attributes for Online Product Comparison"],"prefix":"10.1007","author":[{"given":"Nikhita","family":"Vedula","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcus","family":"Collins","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eugene","family":"Agichtein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oleg","family":"Rokhlenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"27_CR1","doi-asserted-by":"crossref","unstructured":"Bing, L., Wong, T., Lam, W.: Unsupervised extraction of popular product attributes from e-commerce web sites by considering customer reviews. ACM TOIT (2016)","DOI":"10.1145\/2857054"},{"key":"27_CR2","unstructured":"Buhrmester, M., Kwang, T., Gosling, S.: Amazon\u2019s mechanical Turk: a new source of inexpensive, yet high-quality data? (2016)"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Cai, F., de Rijke, M.: A survey of query auto completion in information retrieval. Found. Trends Inf. Retrieval (2016)","DOI":"10.1561\/9781680832013"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: Yake! keyword extraction from single documents using multiple local features. Inf. Sci. (2020)","DOI":"10.1016\/j.ins.2019.09.013"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: ACM SIGIR (1998)","DOI":"10.1145\/290941.291025"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Carmel, D., Lewin-Eytan, L., Maarek, Y.: Product question answering using customer generated content-research challenges. In: ACM SIGIR (2018)","DOI":"10.1145\/3209978.3210203"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Chen, G., Tian, Y., Song, Y.: Joint aspect extraction and sentiment analysis with directional graph convolutional networks. In: COLING (2020)","DOI":"10.18653\/v1\/2020.coling-main.24"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Chen, S., Li, C., Ji, F., Zhou, W., Chen, H.: Driven answer generation for product-related questions in e-commerce. In: ACM WSDM (2019)","DOI":"10.1145\/3289600.3290971"},{"issue":"1","key":"27_CR9","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen, J.: A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20(1), 37\u201346 (1960)","journal-title":"Educ. Psychol. Meas."},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Da\u2019u, A., Salim, N.: Aspect extraction on user textual reviews using multi-channel convolutional neural network. PeerJ Comput. Sci. 5 (2019)","DOI":"10.7717\/peerj-cs.191"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Ghani, R., Probst, K., Liu, Y., Krema, M., Fano, A.: Text mining for product attribute extraction. ACM SIGKDD Explor. Newsl. (2006)","DOI":"10.1145\/1147234.1147241"},{"key":"27_CR12","doi-asserted-by":"crossref","unstructured":"Giannakopoulos, A., Musat, C., Hossmann, A., Baeriswyl, M.: Unsupervised aspect term extraction with B-LSTM & CRF using automatically labelled datasets. arXiv preprint arXiv:1709.05094 (2017)","DOI":"10.18653\/v1\/W17-5224"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An unsupervised neural attention model for aspect extraction. In: ACL (2017)","DOI":"10.18653\/v1\/P17-1036"},{"key":"27_CR14","unstructured":"Hirschmeier, S., Egger, M.: Social product search-enhancing product search with mined (sparse) product features (2018)"},{"key":"27_CR15","doi-asserted-by":"crossref","unstructured":"Huynh, V.P., Papotti, P.: A benchmark for fact checking algorithms built on knowledge bases. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 689\u2013698 (2019)","DOI":"10.1145\/3357384.3358036"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. TOIS (2002)","DOI":"10.1145\/582415.582418"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Kozareva, Z., Li, Q., Zhai, K., Guo, W.: Recognizing salient entities in shopping queries. In: ACL (2016)","DOI":"10.18653\/v1\/P16-2018"},{"key":"27_CR18","unstructured":"Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data (2001)"},{"key":"27_CR19","doi-asserted-by":"crossref","unstructured":"Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: WWW (2005)","DOI":"10.1145\/1060745.1060797"},{"key":"27_CR20","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized Bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Luo, L., et al.: Unsupervised neural aspect extraction with sememes. In: IJCAI (2019)","DOI":"10.24963\/ijcai.2019\/712"},{"key":"27_CR22","unstructured":"More, A.: Attribute extraction from product titles in ecommerce. arXiv preprint arXiv:1608.04670 (2016)"},{"key":"27_CR23","doi-asserted-by":"crossref","unstructured":"Ni, J., Li, J., McAuley, J.: Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In: EMNLP-IJCNLP (2019)","DOI":"10.18653\/v1\/D19-1018"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Petrovski, P., Bizer, C.: Extracting attribute-value pairs from product specifications on the web. In: International Conference on Web Intelligence (2017)","DOI":"10.1145\/3106426.3106449"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: Proceedings of the 10th International Workshop on Semantic Evaluation (2016)","DOI":"10.18653\/v1\/S16-1002"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (2014)","DOI":"10.3115\/v1\/S14-2004"},{"key":"27_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-754-1_2","volume-title":"Natural Language Processing and Text Mining","author":"A Popescu","year":"2007","unstructured":"Popescu, A., Etzioni, O.: Extracting product features and opinions from reviews. In: Kao, A., Poteet, S.R. (eds.) Natural Language Processing and Text Mining. Springer, London (2007). https:\/\/doi.org\/10.1007\/978-1-84628-754-1_2"},{"key":"27_CR28","unstructured":"Probst, K., Ghani, R., Krema, M., Fano, A., Liu, Y.: Semi-supervised learning of attribute-value pairs from product descriptions. In: IJCAI (2007)"},{"key":"27_CR29","unstructured":"Putthividhya, D., Hu, J.: Bootstrapped named entity recognition for product attribute extraction. In: EMNLP (2011)"},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-Bert: sentence embeddings using Siamese Bert-networks. In: EMNLP-IJCNLP (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"27_CR31","unstructured":"Retail, T.: They say they want a revolution - price water house (2016). https:\/\/www.pwc.es\/es\/publicaciones\/retail-y-consumo\/assets\/total-retail-2016.pdf"},{"key":"27_CR32","unstructured":"Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP (2013)"},{"key":"27_CR33","doi-asserted-by":"crossref","unstructured":"Thorne, J., Vlachos, A.: Evidence-based factual error correction. arXiv preprint arXiv:2106.01072 (2021)","DOI":"10.18653\/v1\/2021.acl-long.256"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Tulkens, S., van Cranenburgh, A.: Embarrassingly simple unsupervised aspect extraction. ArXiv abs\/2004.13580 (2020)","DOI":"10.18653\/v1\/2020.acl-main.290"},{"key":"27_CR35","doi-asserted-by":"crossref","unstructured":"Vedula, N., Parthasarathy, S.: Face-keg: fact checking explained using knowledge graphs. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 526\u2013534 (2021)","DOI":"10.1145\/3437963.3441828"},{"key":"27_CR36","doi-asserted-by":"crossref","unstructured":"Wu, B., Cheng, X., Wang, Y., Guo, Y., Song, L.: Simultaneous product attribute name and value extraction from web pages. In: ACM WI-IAT (2009)","DOI":"10.1109\/WI-IAT.2009.286"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, B., Shu, L., Yu, P.S.: Double embeddings and CNN-based sequence labeling for aspect extraction. ArXiv abs\/1805.04601 (2018)","DOI":"10.18653\/v1\/P18-2094"},{"key":"27_CR38","unstructured":"Yang, Y., Chen, W., Li, Z., He, Z., Zhang, M.: Distantly supervised NER with partial annotation learning and reinforcement learning. In: International Conference on Computational Linguistics (2018)"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Zheng, G., Mukherjee, S., Dong, X., Li, F.: OpenTag: open attribute value extraction from product profiles. In: ACM SIGKDD (2018)","DOI":"10.1145\/3219819.3219839"},{"key":"27_CR40","unstructured":"Zhu, M.: Recall, precision and average precision. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo (2004)"}],"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\/978-3-030-99739-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:37:58Z","timestamp":1710257878000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99739-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030997380","9783030997397"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99739-7_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"5 April 2022","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":"Stavanger","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2022.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":"395","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":"35","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":"29","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":"9% - 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":"3","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":"4-6","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":"Additionally, there are other papers: 11 reproducibility, 12 doctoral, 13 CLEF Labs, 5 workshops and 4 tutorials.","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)"}}]}}