Abstract
Implicit feedback techniques may be used for query intent detection, taking advantage of user behavior to understand their interests and preferences. In sponsored search, a primary concern is the user’s interest in purchasing or utilizing a commercial service, or what is called online commercial intent. In this paper, we develop a methodology for employing the content of search engine result pages (SERPs), along with the information obtained from query strings, to study characteristics of query intent, with a particular focus on sponsored search. Our work represents a step toward the development and evaluation of an ontology for commercial search, considering queries that reference specific products, brands, and retailers. Characteristics of query categories are studied with respect to aggregated user clickthrough behavior on advertising links. We present a model for clickthrough behavior that considers the influence of such factors as the location of ads and the rank of ads, along with query category. We evaluate our work using a large corpus of clickthrough data obtained from a major commercial search engine. In addition, the impact of query intent is studied on clickthrough rate, where a baseline model and the query intent model are compared for the purpose of calculating an expected ad clickthrough rate. Our findings suggest that query-based features, along with the content of SERPs, are effective in detecting query intent. Factors such as query category, the rank of an ad, and the total number of ads displayed on a result page relate to the context of the ad, rather than its content. We demonstrate that these context-related factors can have a major influence on expected clickthrough rate, suggesting that these factors should be taken into consideration when the performance of an ad is evaluated.
Similar content being viewed by others
References
Agichtein E, Brill E, Dumais S, Ragno R (2006) Learning user interaction models for predicting web search result preferences. In: Proceedings of the 29th ACM SIGIR conference on research and development in information retrieval, pp 3–10
Ashkan A, Clarke C (2009a) Characterizing commercial intent. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 78–87
Ashkan A, Clarke C (2009b) Term-based commercial intent analysis. In: Proceedings of the 32nd ACM SIGIR conference on research and development in information retrieval, pp 800–801
Ashkan A, Clarke C, Agichtein E, Guo Q (2008) Characterizing query intent from sponsored search clickthrough data. In: Proceedings of the SIGIR workshop on informational retrieval for advertising, pp 15–22
Ashkan A, Clarke C, Agichtein E, Guo Q (2009a) Classifying and characterizing query intent. In: Proceedings of the 31st European conference on information retrieval, pp 578–586
Ashkan A, Clarke C, Agichtein E, Guo Q (2009b) Estimating ad clickthrough rate through query intent analysis. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence, pp 222–229
Baeza-Yates R, Calderán-Benavides L, González-Caro C (2006) The intention behind web queries. In: Proceedings of the 13th international symposium on string processing and information retrieval, pp 98–109
Beitzel S, Jensen E, Frieder O, Grossman D, Lewis D, Chowdhury A, Kolcz A (2005) Automatic web query classification using labeled and unlabeled training data. In: Proceedings of the 28th ACM SIGIR conference on research and development in information retrieval, pp 581–582
Beyond Search-Semantic Computing and Internet Economics Program (2007). http://research.microsoft.com/en-us/um/redmond/about/collaboration/awards/beyondsearchawards.aspx
Brenes DJ, Avello DG, González KP (2009) Survey and evaluation of query intent detection methods. In: WSCD ’09: proceedings of the 2009 workshop on web search click data, pp 1–7
Briggs R, Hollis N (1997) Advertising on the web: is there response before clickthrough. J Advert Res 37(2):33–46
Broder A (2002) A taxonomy of web search. SIGIR Forum 36(2): 3–10
Broder A, Ciaramita M, Fontoura M, Gabrilovich E, Josifovski V, Metzler D, Murdock V, Plachouras V (2008) To swing or not to swing: learning when (not) to advertise. In: Proceedings of the 17th ACM conference on information and knowledge management
Broder A, Fontoura M, Gabrilovich E, Joshi A, Josifovski V, Zhang T (2007a) Robust classification of rare queries using web knowledge. In: Proceedings of the 30th ACM SIGIR conference on research and development in information retrieval, pp 231–238
Broder A, Fontoura M, Josifovski V, Riedel L (2007b) A semantic approach to contextual advertising. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 559–566
Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1): 37–46
Cover T, Thomas J (2006) Elements of information theory. Wiley-Interscience, New York
Dai H, Zhao L, Nie Z, Wen J, Wang L, Li Y (2006) Detecting online commercial intention (OCI). In: Proceedings of the 15th international world wide web conference, pp 829–837
Debmbsczynski K, Kotlowski W, Weiss D (2008) Predicting ads clickthrough rate with decision rules. In: Proceedings of the WWW 2008 workshop on target and ranking for online advertising
Dumais S, Chen H (2000) Hierarchical classification of web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval, pp 256–263
Fain DC, Pedersen JO (2006) Sponsored search: a brief history. Bull Am Soc Inf Sci Technol 32(2): 12–13
Fan TK, Chang CH (2010) Sentiment-oriented contextual advertising. Knowl Inf Syst 33(3): 321–344
Fleiss J, Cohen J (1973) The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability. Educ Psychol Meas 33(3): 613–619
Ghose A, Yang S (2008) An empirical analysis of sponsored search performance in search engine advertising. In: Proceedings of the international conference on web search and data mining, pp 241–250
Graepel T, Candela J, Borchert T, Herbrich R (2010) Web-scale Bayesian click-through rate prediction for sponsored search advertising in microsofts bing search engine. In: Proceedings of the 27th international conference on machine learning
Grant M, Boyd S (2011) CVX: Matlab software for disciplined convex programming http://stanford.edu/~boyd/cvx
Hu Y, Koren Y, Volinsky C (2008) Collaborative filtering for implicit feedback datasets In: Proceedings of the 8th IEEE international conference on data mining, pp 263–272
Jansen B (2007) The comparative effectiveness of sponsored and nonsponsored links for web e-commerce queries. ACM Trans Web 1(1)
Jansen B, Brown A, Resnick M (2007) Factors relating to the decision to click on a sponsored link. Decis Support Syst 44(1): 46–59
Jansen B, Resnick M (2006) An examination of searcher’s perceptions of nonsponsored and sponsored links during ecommerce web searching. J Am Soc Inf Sci Technol 57(14): 1949–1961
Joachims T (2011) SVM light support vector machine. http://svmlight.joachims.org
Joachims T, Granka L, Pan B, Hembrooke H, Gay G (2005) Accurately interpreting clickthrough data as implicit feedback. In: Proceedings of the 28th ACM SIGIR conference on research and development in information retrieval, pp 154–161
Kang I (2005) Transactional query identification in web search. In: Proceedings of the second asia conference on asia information retrieval technology, pp 221–232
Lacerda A, Cristo M, Gonçalves M, 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 549–556
Landis J, Koch G (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159–174
Lee U, Liu Z, Cho J (2005) Automatic identification of user goals in web search. In: Proceedings of the 14th international world wide web conference, pp 391–400
Li X, Wang Y, Acero A. (2008) Learning query intent from regularized click graphs. In: Proceedings of the 31st ACM SIGIR conference on research and development in information retrieval, pp 339–346
Li Y, Krishnamurthy R, Vaithyanathan S, Jagadish HV (2006) Getting work done on the web: supporting transactional queries. In: SIGIR ’06: proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, pp 557–564
Malik HH, Fradkin D, Moerchen F (2011) Single pass text classification by direct feature weighting. Knowl Inf Syst 28(1): 79–98
Nettleton D, Calderán-Benavides L, Baeza-Yates R (2007) Analysis of web search engine query session and clicked documents. In: Proceedings of the 8th international workshop on knowledge discovery on the web, pp 207–226
Poblete B, Baeza-Yates R (2008) Query-sets: using implicit feedback and query patterns to organize web documents. In: Proceedings of the 17th international world wide web conference, pp 41–50
Regelson M, Fain D (2006) Predicting clickthrough rate using keyword clusters. In: Proceedings of the 2nd workshop on sponsored search auctions
Richardson M (2008) Learning about the world through long-term query logs. ACM Trans Web 2(4): 1–27
Richardson M, Dominowska E, Ragno R (2007) Predicting clicks: estimating the clickthrough rate for new ads. In: Proceedings of the 16th international world wide web conference, pp 521–530
Rose D, Levinson D (2004) Understanding user goals in web search. In: Proceedings of the 13th international world wide web conference, pp 13–19
Sculley D (2010) Combined regression and ranking. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, pp 979–988
Sculley D, Malkin R, Basu S, Bayardo R (2009) Predicting bounce rates in sponsored search advertisements. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1325–1334
Shen D, Li Y, Li X, Zhou D (2009) Product query classification. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 741–750
Sparck Jones K, Walker S, Robertson S (2000) A probabilistic model of information retrieval: development and comparative experiments (part 2). Inf Process Manag 36(6): 809–840
Tamine-Lechani L, Boughanem M, Daoud M (2010) Evaluation of contextual information retrieval effectiveness: overview of issues and research. Knowl Inf Syst 24(1): 1–34
Tan B, Peng F (2008) Unsupervised query segmentation using generative language models and wikipedia. In: Proceedings of the 17th international world wide web conference, pp 347–356
Teevan J, Dumais S, Liebling D (2008) To personalize or not to personalize: modeling queries with variation in user intent. In: Proceedings of the 31st ACM SIGIR conference on research and development in information retrieval, pp 163–170
Yih W, Goodman J, Carvalho V (2006) Finding advertising keywords on web pages. In: Proceedings of the 15th international world wide web conference, pp 213–222
Zhu Z, Chen W, Minka T, Zhu C, Chen Z (2010) A novel click model and its applications to online advertising. In: Proceedings of the third ACM international conference on web search and data mining, pp 321–330
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ashkan, A., Clarke, C.L.A. Impact of query intent and search context on clickthrough behavior in sponsored search. Knowl Inf Syst 34, 425–452 (2013). https://doi.org/10.1007/s10115-012-0485-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-012-0485-x