{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:25:19Z","timestamp":1775665519051,"version":"3.50.1"},"reference-count":202,"publisher":"Emerald","issue":"4-5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,10,29]]},"abstract":"<jats:p>Computational Advertising, popularly known as online advertising or Web advertising, refers to finding the most relevant ads matching a particular context on the Web. The context depends on the type of advertising and could mean \u2013 content where the ad is shown, the user who is viewing the ad or the social network of the user. Computational Advertising (CA) is a scientific sub-discipline at the intersection of information retrieval, statistical modeling, machine learning, optimization, large scale search and text analysis. The core problem addressed in Computational Advertising is of match-making between the ads and the context.<\/jats:p>\n                  <jats:p>CA is prevalent in three major forms on the Web. One of the forms involves showing textual ads relevant to a query on the search page, known as Sponsored Search. On the other hand, showing textual ads relevant to a third party webpage content is known as Contextual Advertising. The third form of advertising also deals with the placement of ads on third party webpages, but the ads in this form are rich multimedia ads \u2013 image, video, audio, flash. The business model with rich media ads is slightly different from the ones with textual ads. These ads are also called banner ads, and this form of advertising is known as Display Advertising.<\/jats:p>\n                  <jats:p>Both Sponsored Search and Contextual Advertising involve retrieving relevant ads for different types of content (query and Web page). As ads are short and are mainly written to attract the user, retrieval of ads pose challenges like vocabulary mismatch between the query\/content and the ad. Also, as the user\u2019s probability of examining an ad decreases with the position of the ad in the ranked list, it is imperative to keep the best ads at the top positions. Display Advertising poses several challenges including modeling user behaviour and noisy page content and bid optimization on the advertiser\u2019s side. Additionally, online advertising faces challenges like false bidding, click spam and ad spam. These challenges are prevalent in all forms of advertising. There has been a lot of research work published in different areas of CA in the last one and a half decade. The focus of this survey is to discuss the problems and solutions pertaining to the information retrieval, machine learning and statistics domain of CA. This survey covers techniques and approaches that deal with several issues mentioned above.<\/jats:p>\n                  <jats:p>Research in Computational Advertising has evolved over time and currently continues both in traditional areas (vocabulary mismatch, query rewriting, click prediction) and recently identified areas (user targeting, mobile advertising, social advertising). In this study, we predominantly focus on the problems and solutions proposed in traditional areas in detail and briefly cover the emerging areas in the latter half of the survey. To facilitate future research, a discussion of available resources, list of public benchmark datasets and future directions of work is also provided in the end.<\/jats:p>","DOI":"10.1561\/1500000045","type":"journal-article","created":{"date-parts":[[2014,10,29]],"date-time":"2014-10-29T12:16:59Z","timestamp":1414585019000},"page":"263-418","source":"Crossref","is-referenced-by-count":24,"title":["Computational Advertising: Techniques for Targeting Relevant Ads"],"prefix":"10.1561","volume":"8","author":[{"given":"Kushal","family":"Dave","sequence":"first","affiliation":[{"name":"LTRC International Institute of Information Technology Hyderabad,","place":["India"]}]},{"given":"Vasudeva","family":"Varma","sequence":"additional","affiliation":[{"name":"LTRC International Institute of Information Technology Hyderabad,","place":["India"]}]}],"member":"140","published-online":{"date-parts":[[2014,10,29]]},"reference":[{"issue":"4","key":"2026040314234375800_ref001","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","volume":"7","author":"A Simplex Method for Function Minimization","year":"1965","journal-title":"The Computer Journal"},{"key":"2026040314234375800_ref002","volume-title":"Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising","author":"ADKDD","year":"2009"},{"key":"2026040314234375800_ref003","volume-title":"Proceedings of the Fourth International Workshop on Data Mining for Online Advertising","author":"ADKDD","year":"2010"},{"key":"2026040314234375800_ref004","volume-title":"Proceedings of the Fifth International Workshop on Data Mining for Online Advertising","author":"ADKDD","year":"2011"},{"key":"2026040314234375800_ref005","volume-title":"Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy","author":"ADKDD","year":"2012"},{"key":"2026040314234375800_ref006","volume-title":"Proceedings of the Seventh International Workshop on Data Mining for Online Advertising","author":"ADKDD","year":"2013"},{"key":"2026040314234375800_ref007","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1145\/1835804.1835834","volume-title":"Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201910","author":"Agarwal","year":"2010"},{"key":"2026040314234375800_ref008","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1145\/2556195.2556252","volume-title":"Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM \u201914","author":"Agarwal","year":"2014"},{"key":"2026040314234375800_ref009","first-page":"13","volume-title":"Proceedings of the 22nd international conference on World Wide Web, WWW \u201913","author":"Agrawal","year":"2013"},{"key":"2026040314234375800_ref010","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1145\/2020408.2020433","volume-title":"Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201911","author":"Ahmed","year":"2011"},{"key":"2026040314234375800_ref011","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1145\/1321440.1321488","volume-title":"Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM \u201907","author":"Anagnostopoulos","year":"2007"},{"key":"2026040314234375800_ref012","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1145\/1645953.1646267","volume-title":"Proceeding of the 18th ACM conference on Information and knowledge management, CIKM \u201909","author":"Anastasakos","year":"2009"},{"key":"2026040314234375800_ref013","first-page":"88","volume-title":"Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR \u201903","author":"Anick","year":"2003"},{"key":"2026040314234375800_ref014","unstructured":"Azin Ashkan and CharlesClarke. 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