Abstract
The problem of diversifying search results has attracted much attention, since diverse results can provide non-redundant information and cover multiple query-related topics. However, existing approaches typically assign equal importance to each topic. In this paper, we propose a novel method for diversification: proportionally diversifying search results. Specifically, we study the problem of returning a top-k ranked list where the number of candidates in each topic is proportional to the popularity degree of that topic with respect to the query. We obtain such a top-k proportionally diverse list by maximizing our proposed objective function and we prove that this is an NP-hard problem. We further propose a greedy heuristic to efficiently obtain a good approximate solution. To evaluate the effectiveness of our model, we also propose a novel metric based on the concept of proportionality. Extensive experimental evaluations over our proposed metric as well as standard measures demonstrate the effectiveness and efficiency of our method.
This is a preview of subscription content, log in via an institution to check access.
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 (2009)
Bahmani, B., Chowdhury, A., Goel, A.: Divdb: A system for diversifying query results. In: PVLDB, pp. 1395–1398 (2011)
Carbonell, J., Goldstein, J.: The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)
Chapelle, O., Metlzer, D., Zhang, Y., Grinspan, P.: Expected reciprocal rank for graded relevance. In: CIKM (2009)
Clarke, C., Craswell, N., Soboroff, I.: Overview of the trec 2009 web track. In: TREC (2009)
Clarke, C., Craswell, N., Soboroff, I., Ashkan, A.: A comparative analysis of cascade measures for novelty and diversity. In: WSDM (2011)
Clarke, C., Kolla, M., Cormack, G., Vechtomova, O., Ashkan, A., Buttcher, S., MacKinnon, I.: Novelty and diversity in information retreival evaluation. In: SIGIR (2008)
Clarke, C.L.A., Kolla, M., Vechtomova, O.: An effectiveness measure for ambiguous and underspecified queries. In: Azzopardi, L., Kazai, G., Robertson, S., Rüger, S., Shokouhi, M., Song, D., Yilmaz, E. (eds.) ICTIR 2009. LNCS, vol. 5766, pp. 188–199. Springer, Heidelberg (2009)
Croft, W., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice (2009)
Dang, V., Croft, W.B.: Diversity by proportionality: an election-based approach to search result diversification. In: SIGIR (2012)
Fogaras, D., Rácz, B., Csalogány, K., Sarlós, T.: Towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments. Internet Mathematics 2(3), 333–358 (2005)
Gallagher, M.: Proportionality, disproportionality and electoral systems. Electoral Studies 10(1), 33–51 (1991)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness (1979)
Mei, Q., Guo, J., Radev, D.: Divrank: the interplay of prestige and diversity in information networks. In: ACM SIGKDD, pp. 1009–1018 (2010)
Nemhauser, G., Wolsey, L., Fisher, M.: An analysis of approximations for maximizing submodular set functions. Mathematical Programming 14, 265–294 (1978)
Radlinski, F., Dumais, S.: Improving personalized web search using result diversification. In: SIGIR (2006)
Rafiei, D., Bharat, K., Shukia, A.: Diversifying web search using result diversification. In: WWW (2010)
Santos, R., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: WWW (2010)
Slivkins, A., Radlinski, F., Gollapudi, S.: Learning optimally diverse rankings over large document collections. In: ICML (2010)
Tong, H., He, J., Wen, Z., Konuru, R., Lin, C.-Y.: Diversified ranking on large graphs: An optimization viewpoint. In: ACM SIGKDD, pp. 1028–1036 (2011)
Zhai, C., Cohen, W.W., Lafferty, J.: Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In: ACM SIGIR, pp. 10–17 (2003)
Zhu, X., Goldberg, A.B., Gael, J.V., Andrzejewski, D.: Improving diversity in ranking using absorbing random walks. In: HLT-NAACL, pp. 97–104 (2007)
Ziegler, C.-N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW, pp. 22–32 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, L., Wang, Y., Shepherd, J., Zhao, X. (2013). An Optimization Method for Proportionally Diversifying Search Results. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-37453-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37452-4
Online ISBN: 978-3-642-37453-1
eBook Packages: Computer ScienceComputer Science (R0)