{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:22:42Z","timestamp":1781108562173,"version":"3.54.1"},"reference-count":22,"publisher":"IGI Global Scientific Publishing","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,10,1]]},"abstract":"<p>With the rapid advancement of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from online advertising community. This paper aims to address the major challenges with user queries in the context of behavioral targeting advertising by proposing a query enhancement mechanism that augments user's queries by leveraging a user query log. The empirical evaluation demonstrates that the authors' methodology for query enhancement achieves greater improvement than the baseline models in both intent-based user classification and user segmentation. Different from traditional user segmentation methods, which take little semantics of user behaviors into consideration, the authors propose a novel user segmentation strategy by incorporating the query enhancement mechanism with a topic model to mine the relationships between users and their behaviors in order to segment users in a semantic manner. Comparing with a classical clustering algorithm, K-means, the experimental results indicate that the proposed user segmentation strategy helps improve behavioral targeting effectiveness significantly. This paper also proposes an alternative to define user's search intent for the evaluation purpose, in the case that the dataset is sanitized. This approach automatically labels users in a click graph, which are then used in training an intent-based user classifier.<\/p>","DOI":"10.4018\/ijirr.2013100101","type":"journal-article","created":{"date-parts":[[2014,6,17]],"date-time":"2014-06-17T11:27:21Z","timestamp":1403004441000},"page":"1-17","source":"Crossref","is-referenced-by-count":0,"title":["Intent-Based User Segmentation with Query Enhancement"],"prefix":"10.4018","volume":"3","author":[{"given":"Wei","family":"Xiong","sequence":"first","affiliation":[{"name":"Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Recce","sequence":"additional","affiliation":[{"name":"Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brook","family":"Wu","sequence":"additional","affiliation":[{"name":"Information Systems Department, New Jersey Institute of Technology, Newark, NJ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijirr.2013100101-0","first-page":"993","article-title":"Latent dirichlet allocation.","volume":"3","author":"D. M.Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"ijirr.2013100101-1","doi-asserted-by":"publisher","DOI":"10.1109\/SITIS.2011.19"},{"key":"ijirr.2013100101-2","doi-asserted-by":"crossref","unstructured":"Chen, Y., Pavlov, D., & Canny, J. F. (2009). Large-scale behavioral targeting. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 209\u2013218). Retrieved from http:\/\/cc.gatech.edu\/~zha\/CSE8801\/ad\/p209-chen.pdf","DOI":"10.1145\/1557019.1557048"},{"key":"ijirr.2013100101-3","doi-asserted-by":"crossref","unstructured":"Craswell, N., & Szummer, M. (2007). Random walks on the click graph. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 239\u2013246). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1277784","DOI":"10.1145\/1277741.1277784"},{"key":"ijirr.2013100101-4","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4573(99)00056-4"},{"key":"ijirr.2013100101-5","doi-asserted-by":"crossref","unstructured":"Joachims, T., Granka, L., Pan, B., Hembrooke, H., & Gay, G. (2005). Accurately interpreting clickthrough data as implicit feedback. In Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 154\u2013161). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1076063","DOI":"10.1145\/1076034.1076063"},{"issue":"7","key":"ijirr.2013100101-6","first-page":"881","article-title":"An efficient k-means clustering algorithm: Analysis and implementation. Pattern Analysis and Machine Intelligence","volume":"24","author":"T.Kanungo","year":"2002","journal-title":"IEEE Transactions on"},{"key":"ijirr.2013100101-7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2005.07.036"},{"key":"ijirr.2013100101-8","doi-asserted-by":"crossref","unstructured":"Lacerda, A., & Cristo, M. Gon\\ccalves, M. A., Fan, W., Ziviani, N., & Ribeiro-Neto, B. (2006). Learning to advertise. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 549\u2013556). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1148265","DOI":"10.1145\/1148170.1148265"},{"key":"ijirr.2013100101-9","doi-asserted-by":"crossref","unstructured":"Liu, N., Yan, J., Shen, D., Chen, D., Chen, Z., & Li, Y. (2010). Learning to rank audience for behavioral targeting. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 719\u2013720). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1835582","DOI":"10.1145\/1835449.1835582"},{"key":"ijirr.2013100101-10","doi-asserted-by":"crossref","unstructured":"Pass, G., Chowdhury, A., & Torgeson, C. (2006). A picture of search. In Proceedings of the 1st International Conference on Scalable Information Systems (p. 1). Retrieved from http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.92.3074&rep=rep1&type=pdf","DOI":"10.1145\/1146847.1146848"},{"key":"ijirr.2013100101-11","unstructured":"Ratnaparkhi, A. (1992). Finding predictive search queries for behavioral targeting. Training, 10(27,920,032,253), 27\u2013920."},{"key":"ijirr.2013100101-12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2005.04.048"},{"key":"ijirr.2013100101-13","doi-asserted-by":"publisher","DOI":"10.1016\/0306-4573(88)90021-0"},{"key":"ijirr.2013100101-14","doi-asserted-by":"crossref","unstructured":"Spink, A., Ozmutlu, S., Ozmutlu, H. C., & Jansen, B. J. (2002). US versus European web searching trends. ACM SIGIR Forum (Vol. 36, pp. 32\u201338). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=792555","DOI":"10.1145\/792550.792555"},{"key":"ijirr.2013100101-15","unstructured":"Wang, C., Zhang, P., Choi, R., & Eredita, M. D. (2002). Understanding consumers attitude toward advertising. In Proceedings of the Eighth Americas Conference on Information Systems (pp. 1143\u20131148). Retrieved from http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10.1.1.12.8755&rep=rep1&type=pdf"},{"key":"ijirr.2013100101-16","doi-asserted-by":"crossref","unstructured":"Wang, X. J., Yu, M., Zhang, L., Cai, R., & Ma, W. Y. (2009). Argo: Intelligent advertising by mining a user\u2019s interest from his photo collections. In Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising (pp. 18\u201326). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1592752","DOI":"10.1145\/1592748.1592752"},{"key":"ijirr.2013100101-17","doi-asserted-by":"crossref","unstructured":"Wen, J. R., Nie, J. Y., & Zhang, H. J. (2001). Clustering user queries of a search engine. In Proceedings of the 10th International Conference on World Wide Web (pp. 162\u2013168). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=371974","DOI":"10.1145\/371920.371974"},{"key":"ijirr.2013100101-18","doi-asserted-by":"publisher","DOI":"10.1145\/503104.503108"},{"key":"ijirr.2013100101-19","doi-asserted-by":"publisher","DOI":"10.1109\/ICINA.2010.5636772"},{"key":"ijirr.2013100101-20","doi-asserted-by":"crossref","unstructured":"Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., & Chen, Z. (2009). How much can behavioral targeting help online advertising? In Proceedings of the 18th International Conference on World Wide Web (pp. 261\u2013270). Retrieved from http:\/\/dl.acm.org\/citation.cfm?id=1526745","DOI":"10.1145\/1526709.1526745"},{"key":"ijirr.2013100101-21","doi-asserted-by":"publisher","DOI":"10.1109\/ICICSE.2012.40"}],"container-title":["International Journal of Information Retrieval Research"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=109659","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T13:32:37Z","timestamp":1654090357000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/ijirr.2013100101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2013,10,1]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2013,10]]}},"URL":"https:\/\/doi.org\/10.4018\/ijirr.2013100101","relation":{},"ISSN":["2155-6377","2155-6385"],"issn-type":[{"value":"2155-6377","type":"print"},{"value":"2155-6385","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,10,1]]}}}