{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T06:19:31Z","timestamp":1775283571757,"version":"3.50.1"},"reference-count":33,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2009,4,1]],"date-time":"2009-04-01T00:00:00Z","timestamp":1238544000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2009,4]]},"abstract":"<jats:p>\n            Question Answering Communities such as Naver, Baidu Knows, and Yahoo! Answers have emerged as popular, and often effective, means of information seeking on the web. By posting questions for other participants to answer, information seekers can obtain specific answers to their questions. Users of CQA portals have already contributed millions of questions, and received hundreds of millions of answers from other participants. However, CQA is not always effective: in some cases, a user may obtain a perfect answer within minutes, and in others it may require hours\u2014and sometimes days\u2014until a satisfactory answer is contributed. We investigate the problem of predicting information seeker satisfaction in collaborative question answering communities, where we attempt to predict whether a question author will be satisfied with the answers submitted by the community participants. We present a general prediction model, and develop a variety of content, structure, and community-focused features for this task. Our experimental results, obtained from a large-scale evaluation over thousands of real questions and user ratings, demonstrate the feasibility of modeling and predicting asker satisfaction. We complement our results with a thorough investigation of the interactions and information seeking patterns in question answering communities that correlate with information seeker satisfaction. We also explore\n            <jats:italic>personalized<\/jats:italic>\n            models of asker satisfaction, and show that when sufficient interaction history exists, personalization can significantly improve prediction accuracy over a \u201cone-size-fits-all\u201d model. Our models and predictions could be useful for a variety of applications, such as user intent inference, answer ranking, interface design, and query suggestion and routing.\n          <\/jats:p>","DOI":"10.1145\/1514888.1514893","type":"journal-article","created":{"date-parts":[[2009,4,21]],"date-time":"2009-04-21T14:14:44Z","timestamp":1240323284000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["Modeling information-seeker satisfaction in community question answering"],"prefix":"10.1145","volume":"3","author":[{"given":"Eugene","family":"Agichtein","sequence":"first","affiliation":[{"name":"Emory University, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yandong","family":"Liu","sequence":"additional","affiliation":[{"name":"Emory University, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"Gerogia Institute of Technology, Atlanta, GA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2009,4,21]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341559"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1148170.1148175"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341557"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1108\/eb026726"},{"key":"e_1_2_2_5_1","volume-title":"Proceedings of the 6th International Conference on User Modelling (UM'97)","author":"Belkin N. 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C. 1998. Fast training of support vector machines using sequential minimal optimization. Advances in Kernal Methods\u2014Support Vector Learning 185--208.   Platt J. C. 1998. Fast training of support vector machines using sequential minimal optimization. Advances in Kernal Methods\u2014Support Vector Learning 185--208.","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Quinlan J. 1996. Improved use of continuous attributes in c4.5. In J. Artif. Intell. Resear.   Quinlan J. 1996. Improved use of continuous attributes in c4.5. In J. Artif. Intell. 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