Abstract
Aggregated search is that task of blending results from different search services, or verticals, into the core web results. Aggregated search coherence is the extent to which results from different sources focus on similar senses of an ambiguous or underspecified query. Prior research studied the effect of aggregated search coherence on search behavior and found that the query-senses in the vertical results can affect user interaction with the web results. In this work, we develop and evaluate algorithms for vertical results selection—deciding which results from a particular vertical to display. Results from a large-scale user study suggest that algorithms that improve the level of coherence between the vertical and web results influence users to make more productive decisions with respect to the web results—to engage with the web results when at least one of them is relevant and, to a lesser extent, to avoid engaging with the web results otherwise.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM, pp. 5–14 (2009)
Arguello, J., Capra, R.: The effect of aggregated search coherence on search behavior. In: CIKM, pp. 1293–1302 (2012)
Arguello, J., Capra, R.: The effects of vertical rank and border on aggregated search coherence and search behavior. In: CIKM, pp. 539–548 (2014)
Arguello, J., Capra, R., Wu, W.-C.: Factors affecting aggregated search coherence and search behavior. In: CIKM, pp. 1989–1998 (2013)
Arguello, J., Diaz, F., Callan, J.: Learning to aggregate vertical results into web search results. In: CIKM, pp. 201–210 (2011)
Arguello, J., Diaz, F., Callan, J., Crespo, J.-F.: Sources of evidence for vertical selection. In: SIGIR, pp. 315–322 (2009)
Bailey, P., Craswell, N., White, R.W., Chen, L., Satyanarayana, A., Tahaghoghi, S.M.: Evaluating search systems using result page context. In: IIiX, pp. 105–114 (2010)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)
Carterette, B., Chandar, P.: Probabilistic models of ranking novel documents for faceted topic retrieval. In: CIKM, pp. 1287–1296 (2009)
Diaz, F.: Integration of news content into web results. In: WSDM, pp. 182–191 (2009)
Diaz, F., Arguello, J.: Adaptation of offline vertical selection predictions in the presence of user feedback. In: SIGIR, pp. 323–330 (2009)
Jeffreys, H.: An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences 186(1007), 453–461 (1946)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Ponnuswami, A.K., Pattabiraman, K., Wu, Q., Gilad-Bachrach, R., Kanungo, T.: On composition of a federated web search result page: Using online users to provide pairwise preference for heterogeneous verticals. In: WSDM, pp. 715–724 (2011)
Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: SIGIR, pp. 691–692 (2006)
Sanderson, M.: Ambiguous queries: test collections need more sense. In: SIGIR, pp. 499–506 (2008)
Santos, R.L.T., Macdonald, C., Ounis, I.: Exploiting query reformulations for Web search result diversification. In: WWW, pp. 881–890 (2010)
Santos, R.L.T., Macdonald, C., Ounis, I.: Aggregated search result diversification. In: ITCIR, pp. 250–261 (2011)
Smucker, M.D., Allan, J., Carterette, B.: A comparison of statistical significance tests for information retrieval evaluation. In: CIKM, pp. 623–632 (2007)
Zhou, K., Cummins, R., Lalmas, M., Jose, J.M.: Evaluating aggregated search pages. In: SIGIR, pp. 115–124 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Arguello, J. (2015). Improving Aggregated Search Coherence. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds) Advances in Information Retrieval. ECIR 2015. Lecture Notes in Computer Science, vol 9022. Springer, Cham. https://doi.org/10.1007/978-3-319-16354-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-16354-3_3
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16353-6
Online ISBN: 978-3-319-16354-3
eBook Packages: Computer ScienceComputer Science (R0)