Information Retrieval Journal

, Volume 19, Issue 5, pp 447–486 | Cite as

Beyond entities: promoting explorative search with bundles

  • Ilaria BordinoEmail author
  • Mounia Lalmas
  • Yelena Mejova
  • Olivier Van Laere


Search engines are increasingly going beyond the pure relevance of search results to entertain users with information items that are interesting and even surprising, albeit sometimes not fully related to their search intent. In this paper, we study this serendipitous search space in the context of entity search, which has recently emerged as a powerful paradigm for building semantically rich answers. Specifically, our work proposes to enhance an explorative search system that represents a large sample of Yahoo Answers as an entity network, with a result structuring that goes beyond ranked lists, using composite entity retrieval, which requires a bundling of the results. We propose and compare six bundling methods, which exploit topical categories, entity specializations, and sentiment, and go beyond simple entity clustering. Two large-scale crowd-sourced studies show that users find a bundled organization—especially based on the topical categories of the query entity—to be better at revealing the most useful results, as well as at organizing the results, helping to discover novel and interesting information, and promoting exploration. Finally, a third study of 30 simulated search tasks reveals the bundled search experience to be less frustrating and more rewarding, with more users willing to recommend it to others.


Entity search Entity networks Composite eetrieval Bundles Explorative aearch Topical bundles 



We are very thankful to Byungkyu Kang for his help in designing the explorative-search user study.


This work was partially funded by LiMoSINe project (

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Adar, E., Teevan, J., Agichtein, E., & Maarek, Y. (Eds.). (2012). Proceedings of the fifth international conference on web search and web data mining, WSDM’2012. Seattle, WA: ACM.Google Scholar
  2. Amer-Yahia, S., Bonchi, F., Castillo, C., Feuerstein, E., Méndez-Díaz, I., & Zabala, P. (2014). Composite retrieval of diverse and complementary bundles. IEEE Transactions on Knowledge and Data Engineering, 26(11), 2662–2675. doi: 10.1109/TKDE.2014.2306678.CrossRefGoogle Scholar
  3. Amitay, E., Carmel, D., Har’El, N., Ofek-Koifman, S., Soffer, A., & Yogev, S. et al. (2009). Social search and discovery using a unified approach. In C. Cattuto, G. Ruffo & F. Menczer (Eds.). Proceedings of the 20th ACM conference on hypertext and hypermedia (HYPERTEXT’2009) (pp. 199–208). Torino: ACM. June 29–July 1, 2009. doi: 10.1145/1557914.1557950.
  4. Angel, A., Chaudhuri, S., Das, G., & Koudas, N. (2009). Ranking objects based on relationships and fixed associations. In M. L. Kersten, B. Novikov, J. Teubner, V. Polutin & S. Manegold (Eds.). Proceedings of ACM 12th international conference on extending database technology (EDBT’2009). ACM international conference proceeding series (Vol. 360, pp. 910–921). Saint Petersburg. March 24–26, 2009. doi: 10.1145/1516360.1516464.
  5. Arguello, J., Wu, W., Kelly, D., & Edwards, A. (2012). Task complexity, vertical display and user interaction in aggregated search. In W. Hersh et al. (Eds.) (pp. 435–444) 2012. doi: 10.1145/2348283.2348343.
  6. Baeza-Yates, R. A. (2010). Searching the web of objects. In A. Dearle & R. Zicari (Eds.). Proceedingsof objects and databases: Third international conference (ICOODB’2010). Lecture notes in computer science (Vol. 6348, pp. 6–7). Frankfurt/Main: Springer. September 28–30, 2010. doi: 10.1007/978-3-642-16092-9_2.
  7. Balog, K., Meij, E. J., & de Rijke, M. (2010). Entity search: Building bridges between two worlds. In Proceedings of the 3rd international semantic search workshop (SEMSEARCH’10) (pp. 1–5). New York, NY: ACM.Google Scholar
  8. Baraglia, R., Morales, G. D. F., & Lucchese, C. (2010). Document similarity self-join with mapreduce. In G. I. Webb et al. (Eds.) (pp. 731–736) 2010. doi: 10.1109/ICDM.2010.70.
  9. Boldi, P., Bonchi, F., Castillo, C., Donato, D., Gionis, A., & Vigna, S. (2008). The query-flow graph: Model and applications. In J. G. Shanahan et al. (Eds.) (pp. 609–618) 2008. doi: 10.1145/1458082.1458163.
  10. Boldi, P., Bonchi, F., Castillo, C., & Vigna, S. (2009). From “dango” to “japanese cakes”: Query reformulation models and patterns. In Main conference proceedings of 2009 IEEE/WIC/ACM international conference on web intelligence (WI’2009) (pp. 183–190). Milan: IEEE Computer Society. September 15–18, 2009. doi: 10.1109/WI-IAT.2009.34.
  11. Bonchi, F., Perego, R., Silvestri, F., Vahabi, H., & Venturini, R. (2012). Efficient query recommendations in the long tail via center-piece subgraphs. In W. Hersh et al. (Eds.) (pp. 345–354) 2012. doi: 10.1145/2348283.2348332.
  12. Bordino, I., Mejova, Y., & Lalmas, M. (2013a). Penguins in sweaters, or serendipitous entity search on user-generated content. In Q. He et al. (Eds.) (pp. 109–118) 2013. doi: 10.1145/2505515.2505680.
  13. Bordino, I., Morales, G. D. F., Weber, I., & Bonchi, F. (2013b). From machu_picchu to “rafting the urubamba river”: Anticipating information needs via the entity-query graph. In S. Leonardi, A. Panconesi, P. Ferragina & A. Gionis (Eds.).Sixth ACM international conference on web search and data mining (WSDM’2013) (pp. 275–284), Rome: ACM. doi: 10.1145/2433396.2433433.
  14. Bordino, I., Van Laere, O., Lalmas, M., & Mejova, Y. (2014). Driving curiosity in search with large-scale entity networks. SIGWEB Newsletter (Autumn),. doi: 10.1145/2682914.2682919.Google Scholar
  15. Borlund, P., & Ingwersen, P. (1997). The development of a method for the evaluation of interactive information retrieval systems. Journal of Documentation, 53(3), 225–250. doi: 10.1108/EUM0000000007198.CrossRefGoogle Scholar
  16. Bota, H., Zhou, K., & Jose, J. M. (2015). Exploring composite retrieval from the users’ perspective. In A. Hanbury, G. Kazai, A. Rauber & N. Fuhr (Eds.). Advances in information retrieval: 37th European conference on IR research (ECIR’2015). Proceedings, lecture notes in computer science (Vol. 9022, pp. 13–24). Vienna. March 29–April 2, 2015. doi: 10.1007/978-3-319-16354-3_2.
  17. Bota, H., Zhou, K., Jose, J. M., & Lalmas, M. (2014). Composite retrieval of heterogeneous web search. In C. Chung, A. Z. Broder, K. Shim & T. Suel (Eds.). 23rd international world wide web conference (WWW’14) (pp. 119–130). Seoul: ACM. April 7–11, 2014. doi: 10.1145/2566486.2567985.
  18. Cao, T., Nguyen, Q., Nguyen, A., & Le, T. (2011). Integrating open data and generating travel itinerary in semantic-aware tourist information system. In D. Taniar, E. Pardede, H. Nguyen, J. W. Rahayu & I. Khalil (Eds.). The 13th international conference on information integration and web-based applications and services (iiWAS’2011) (pp. 214–221). Ho Chi Minh City: ACM. December 5–7, 2011. doi: 10.1145/2095536.2095573.
  19. Capannini, G., Nardini, F. M., Perego, R., & Silvestri, F. (2011). Efficient diversification of web search results. PVLDB 4(7):451–459.
  20. Capra, R., Velasco-Martin, J., & Sams, B. (2011). Collaborative information seeking by the numbers. In Proceedings of the 3rd international workshop on collaborative information retrieval (CIR’11) (pp. 7–10). New York, NY: ACM. doi: 10.1145/2064075.2064078.
  21. Chakrabarti, K., Ganti, V., Han, J., & Xin, D. (2006). Ranking objects based on relationships. In S. Chaudhuri, V. Hristidis & N. Polyzotis (Eds.). Proceedings of the ACM SIGMOD international conference on management of data (pp. 371–382). Chicago, IL: ACM. June 27–29, 2006. doi: 10.1145/1142473.1142516.
  22. Chen, H., & Dumais, S. T. (2000). Bringing order to the web: Automatically categorizing search results. In T. Turner & G. Szwillus (Eds.). Proceedings of the CHI 2000 conference on human factors in computing systems (pp. 145–152). The Hague: ACM. April 1–6, 2000. doi: 10.1145/332040.332418.
  23. Cheng, T., Yan, X., & Chang, K. C. (2007). Entityrank: Searching entities directly and holistically. In C. Koch, J. Gehrke, M. N. Garofalakis, D. Srivastava, K. Aberer & A. Deshpande et al. (Eds.). Proceedings of the 33rd international conference on very large data bases (pp. 387–398). University of Vienna, ACM. September 23–27, 2007.
  24. Craswell, N., & Szummer, M. (2007). Random walks on the click graph. In W. Kraaij, A. P. de Vries, C. L. A. Clarke, N. Fuhr & N. Kando (Eds.). Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval (SIGIR’2007) (pp. 239–246). Amsterdam: ACM. July 23–27, 2007. doi: 10.1145/1277741.1277784.
  25. Cutting, D. R., Pedersen, J. O., Karger, D. R., & Tukey, J. W. (1992). Scatter/gather: A cluster-based approach to browsing large document collections. In N. J. Belkin, P. Ingwersen & A. M. Pejtersen (Eds.). Proceedings of the 15th annual international ACM SIGIR conference on research and development in information retrieval (pp. 318–329). Copenhagen: ACM. June 21–24, 1992. doi: 10.1145/133160.133214.
  26. Deng, T., Fan, W., & Geerts, F. (2012). On the complexity of package recommendation problems. In: M. Benedikt, M. Krötzsch, & M. Lenzerini (Eds.). Proceedings of the 31st ACM SIGMOD–SIGACT–SIGART symposium on principles of database systems (PODS’2012) (pp. 261–272) Scottsdale, AZ: ACM. May 20–24, 2012. doi: 10.1145/2213556.2213592.
  27. Dunn, J. C. (1974). Well-separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4(1), 95–104.MathSciNetCrossRefzbMATHGoogle Scholar
  28. Evans, B. M., & Chi, E. H. (2008). Towards a model of understanding social search. In B. Begole & D. W. McDonald (Eds.). Proceedings of the 2008 ACM conference on computer supported cooperative work (CSCW’2008) (pp. 485–494). San Diego, CA: ACM. November 8–12, 2008. doi: 10.1145/1460563.1460641.
  29. Ferragina, P., & Gulli, A. (2004). The anatomy of snaket: A hierarchical clustering engine for web-page snippets. In J. Boulicaut, F. Esposito, F. Giannotti & D. Pedreschi (Eds.). Knowledge discovery in databases: 8th European conference on principles and practice of knowledge discovery in databases, (PKDD’2004). Proceedings of lecture notes in computer science (Vol. 3202, pp. 506–508). Pisa: Springer. September 20–24, 2004. doi: 10.1007/978-3-540-30116-5_48.
  30. Fujimura, K., Toda, H., Inoue, T., Hiroshima, N., Kataoka, R., & Sugizaki, M. (2006) Blogranger: A multi-faceted blog search engine. In Proceedings of the 3rd annual WWE.Google Scholar
  31. Gamon, M., Yano, T., Song, X., Apacible, J., & Pantel, P. (2013). Identifying salient entities in web pages. In Q. He et al. (Eds.) (pp. 2375–2380) 2013. doi: 10.1145/2505515.2505602.
  32. Grassi, M., Cambria, E., Hussain, A., & Piazza, F. (2011). Sentic web: A new paradigm for managing social media affective information. Cognitive Computation, 3(3), 480–489. doi: 10.1007/s12559-011-9101-8.CrossRefGoogle Scholar
  33. Guo, X., Xiao, C., & Ishikawa, Y. (2012). Combination skyline queries. Trans Large-Scale Data- and Knowledge-Centered Systems, 6, 1–30. doi: 10.1007/978-3-642-34179-3_1.Google Scholar
  34. Hanhart, P., Korshunov, P., & Ebrahimi, T. (2014). Crowd-based quality assessment of multiview video plus depth coding. In 2014 IEEE international conference on image processing (ICIP’2014) (pp. 743–747). Paris: IEEE. October 27–30, 2014. doi: 10.1109/ICIP.2014.7025149.
  35. He, D., Göker, A., & Harper, D. J. (2002). Combining evidence for automatic web session identification. Information Processing and Management, 38(5), 727–742. doi: 10.1016/S0306-4573(01)00060-7.CrossRefzbMATHGoogle Scholar
  36. He, Q., Iyengar, A., Nejdl, W., Pei, J., & Rastogi, R. (Eds.). (2013). In 22nd ACM international conference on information and knowledge management (CIKM’13). San Francisco, CA: ACM. October 27–November 1, 2013.
  37. Held, C., & Cress, U. (2009). Learning by foraging: The impact of social tags on knowledge acquisition. In U. Cress, V. Dimitrova & M. Specht (Eds.). Learning in the synergy of multiple disciplines, 4th European conference on technology enhanced learning (EC-TEL’2009). Proceedings of the lecture notes in computer science (Vol. 5794, pp. 254–266). Nice: Springer. September 29–October 2, 2009. doi: 10.1007/978-3-642-04636-0_24.
  38. Hersh, W. R., Callan, J., Maarek, Y., & Sanderson, M. (Eds.). (2012). The 35th international ACM SIGIR conference on research and development in information retrieval (SIGIR’12), Portland, OR: ACM. August 12–16, 2012.
  39. Hofeld, T., Keimel, C., Hirth, M., Gardlo, B., Habigt, J., & Diepold, K., et al. (2013). Crowdtesting: A novel methodology for subjective user studies and QoE evaluation. Technical report 486, Department of Computer Science.Google Scholar
  40. Hoffart, J., Yosef, M. A., Bordino, I., Fürstenau, H., Pinkal, M., & Spaniol, M., et al. (2011). Robust disambiguation of named entities in text. In Proceedings of the 2011 conference on empirical methods in natural language processing (EMNLP’2011). A meeting of SIGDAT, a special interest group of the ACL (pp. 782–792). Edinburgh: ACL, John McIntyre Conference Centre. July 27–31, 2011.
  41. Iaquinta, L., de Gemmis, M., Lops, P., Semeraro, G., Filannino, M., & Molino, P. (2008). Introducing serendipity in a content-based recommender system. In F. Xhafa, F. Herrera, A. Abraham, M. Köppen & J. M. Benítez (Eds.). 8th International conference on hybrid intelligent systems (HIS’2008) (pp. 168–173). Barcelona: IEEE Computer Society. September 10–12, 2008. doi: 10.1109/HIS.2008.25.
  42. Jansen, B. J., & Pooch, U. W. (2001). A review of web searching studies and a framework for future research. JASIST, 52(3), 235–246. doi: 10.1002/1097-4571(2000)9999:9999<:AID-ASI1607>3.0.CO;2-F.CrossRefGoogle Scholar
  43. Jansen, B. J., Spink, A., Blakely, C., & Koshman, S. (2007). Defining a session on web search engines. JASIST, 58(6), 862–871. doi: 10.1002/asi.20564.CrossRefGoogle Scholar
  44. Jeh, G., & Widom, J. (2003). Scaling personalized web search. In G. Hencsey, B. White, Y. R. Chen, L. Kovács & S. Lawrence (Eds.). Proceedings of the twelfth international world wide web conference (WWW’2003) (pp. 271–279). Budapest: ACM. May 20–24, 2003. doi: 10.1145/775152.775191.
  45. Käki, M. (2005). Findex: Search result categories help users when document ranking fails. In G. C. van der Veer & C. Gale (Eds.). Proceedings of the 2005 conference on human factors in computing systems (CHI’2005) (pp. 131–140). Portland, OR: ACM. April 2–7, 2005. doi: 10.1145/1054972.1054991.
  46. Kashyap, A., & Hristidis, V. (2012). Comprehension-based result snippets. In X. Chen, G. Lebanon, H. Wang & M. J. Zaki (Eds.). 21st ACM international conference on information and knowledge management (CIKM’12) (pp. 1075–1084). Maui, HI: ACM. October 29–November 02, 2012. doi: 10.1145/2396761.2398405.
  47. Keimel, C., Habigt, J., Horch, C., & Diepold, K. (2012). Qualitycrowd: A framework for crowd-based quality evaluation. In M. Domanski, T. Grajek, D. Karwowski & R. Stasinski (Eds.). 2012 picture coding symposium (PCS’2012) (pp. 245–248). Krakow: IEEE. May 7–9, 2012. doi: 10.1109/PCS.2012.6213338.
  48. Kucuktunc, O., Cambazoglu, B. B., Weber, I., & Ferhatosmanoglu, H. (2012). A large-scale sentiment analysis for yahoo! Answers. In E. Adar et al. (Eds.) (pp. 633–642) 2012. doi: 10.1145/2124295.2124371.
  49. Kulkarni, S., Singh, A., Ramakrishnan, G., & Chakrabarti, S. (2009). Collective annotation of wikipedia entities in web text. In J. F. Elder, IV, F. Fogelman-Soulié, P. A. Flach & M. J. Zaki (Eds.) Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 457–466). Paris: ACM. June 28–July 1, 2009. doi: 10.1145/1557019.1557073.
  50. Laere, O. V., Bordino, I., Mejova, Y., & Lalmas, M. (2014). DEESSE: entity-driven exploratory and serendipitous search system. In J. Li, X. S. Wang, M. N. Garofalakis, I. Soboroff, T. Suel & M. Wang (Eds.). Proceedings of the 23rd ACM international conference on conference on information and knowledge management (CIKM’2014) (pp. 2072–2074). Shanghai: ACM. November 3–7, 2014. doi: 10.1145/2661829.2661853.
  51. Lagun, D., & Agichtein, E. (2011). Viewser: A tool for large-scale remote studies of web search result examination. In D. S. Tan, S. Amershi, B. Begole, W. A. Kellogg & M. Tungare (Eds.). Proceedings of the international conference on human factors in computing systems (CHI’2011). Extended abstracts volume (pp. 2035–2040). Vancouver, BC: ACM. May 7–12, 2011. doi: 10.1145/1979742.1979936.
  52. Liu, Y., & Agichtein, E. (2008). On the evolution of the yahoo! answers QA community. In S. Myaeng, D. W. Oard, F. Sebastiani, T. Chua, M. Leong (Eds.). Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval (SIGIR’2008) (pp. 737–738) Singapore: ACM. July 20–24, 2008. doi: 10.1145/1390334.1390478.
  53. Liu, Y., Li, Z., Xiong, H., Gao, X., & Wu, J. (2010). Understanding of internal clustering validation measures. In G. I. Webb (Eds.) et al. (pp. 911–916) 2010. doi: 10.1109/ICDM.2010.35.
  54. Meij, E., Weerkamp, W., & de Rijke, M. (2012). Adding semantics to microblog posts. In E. Adar et al. (Eds.) (pp. 563–572) 2012. doi: 10.1145/2124295.2124364.
  55. Mihalcea, R., & Csomai, A. (2007). Wikify!: Linking documents to encyclopedic knowledge. In M. J. Silva, A. H. F. Laender, R. A. Baeza-Yates, D. L. McGuinness, B. Olstad & Ø. H. Olsen (Eds.). Proceedings of the sixteenth ACM conference on information and knowledge management (CIKM’2007) (pp. 233–242). Lisbon: ACM. November 6–10, 2007. doi: 10.1145/1321440.1321475.
  56. Miliaraki, I., Blanco, R., & Lalmas, M. (2015). From “selena gomez” to “marlon brando”: Understanding explorative entity search. In Proceedings of the 24th international conference on world wide web, (WWW’2015) (pp. 765–775) Florence. May 18–22, 2015. doi: 10.1145/2736277.2741284.
  57. Milne, D. N., & Witten, I. H. (2008). Learning to link with wikipedia. In J. G. Shanahan et al. (Eds.) (pp. 509–518) 2008. doi: 10.1145/1458082.1458150.
  58. O’Brien, H. L. (2010). The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences. Interacting with Computers, 22(5), 344–352. doi: 10.1016/j.intcom.2010.04.001.CrossRefGoogle Scholar
  59. Parameswaran, A. G., Venetis, P., & Garcia-Molina, H. (2011). Recommendation systems with complex constraints: A course recommendation perspective. ACM Transactions on Information Systems, 29(4), 20. doi: 10.1145/2037661.2037665.CrossRefGoogle Scholar
  60. Paranjpe, D. (2009). Learning document aboutness from implicit user feedback and document structure. In D. W. Cheung, I. Song, W. W. Chu, X. Hu & J. J. Lin (Eds.). Proceedings of the 18th ACM conference on information and knowledge management (CIKM’2009) (pp. 365–374). Hong Kong: ACM. November 2–6, 2009. doi: 10.1145/1645953.1646002.
  61. Pirolli, P., Schank, P. K., Hearst, M. A., & Diehl, C. (1996). Scatter/gather browsing communicates the topic structure of a very large text collection. In B. A. Nardi, G. C. van der Veer & M. J. Tauber (Eds.) Proceedings of the conference on human factors in computing systems: Common ground (CHI’96) (pp. 213–220). Vancouver, BC: ACM. April 13–18, 1996. doi: 10.1145/238386.238489.
  62. Pratt, W., & Fagan, L. M. (2000). Research paper: The usefulness of dynamically categorizing search results. JAMIA, 7(6), 605–617. doi: 10.1136/jamia.2000.0070605.Google Scholar
  63. Ribeiro, F. P., Florêncio, D. A. F., & Nascimento, V. H. (2011). Crowdsourcing subjective image quality evaluation. In B. Macq & P. Schelkens (Eds.). 18th IEEE international conference on image processing (ICIP’2011) (pp. 3097–3100). Brussels: IEEE. September 11–14, 2011. doi: 10.1109/ICIP.2011.6116320.
  64. Rose, D. E., & Levinson, D. (2004). Understanding user goals in web search. In S. I. Feldman, M. Uretsky, M. Najork & C. E. Wills (Eds.). Proceedings of the 13th international conference on world wide web (WWW’2004) (pp. 13–19). New York, NY: ACM. May 17–20, 2004. doi: 10.1145/988672.988675.
  65. Rousseeuw, P. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(1), 53–65. doi: 10.1016/0377-0427(87)90125-7.CrossRefzbMATHGoogle Scholar
  66. Roy, S. B., Amer-Yahia, S., Chawla, A., Das, G., & Yu, C. (2010). Constructing and exploring composite items. In A. K. Elmagarmid & D. Agrawal (Eds.). Proceedings of the ACM SIGMOD international conference on management of data (SIGMOD’2010) (pp. 843–854). Indianapolis: ACM. June 6–10 2010. doi: 10.1145/1807167.1807258.
  67. Salton, G., Wong, A., & Yang, C. S. (1975). A vector space model for automatic indexing. Communications of the ACM, 18(11), 613–620. doi: 10.1145/361219.361220.CrossRefzbMATHGoogle Scholar
  68. Shanahan, J. G., Amer-Yahia, S., Manolescu, I., Zhang, Y., Evans, D. A., Kolcz, A., et al. (Eds.). (2008). Proceedings of the 17th ACM conference on information and knowledge management (CIKM’2008) Napa Valley, CA: ACM. October 26–30, 2008.Google Scholar
  69. Snow, R., O’Connor, B., Jurafsky, D., & Ng, A. Y. (2008). Cheap and fast: but is it good? Evaluating non-expert annotations for natural language tasks. In 2008 Proceedings of the conference on empirical methods in natural language processing (EMNLP’2008). A meeting of SIGDAT, a special interest group of the ACL (pp. 254–263). Honolulu, HI: ACL. October 25–27, 2008.
  70. Spink, A., Jansen, B. J., Wolfram, D., & Saracevic, T. (2002). From e-sex to e-commerce: Web search changes. IEEE Computer, 35(3), 107–109. doi: 10.1109/2.989940.CrossRefGoogle Scholar
  71. Stamou, S., & Kozanidis, L. (2009). Towards faceted search for named entity queries. In L. Chen, C. Liu, X. Zhang, S. Wang, D. Strasunskas & S. L. Tomassen et al. (Eds.). Advances in web and network technologies, and information management, APWeb/WAIM 2009 international workshops: WCMT 2009, RTBI 2009, DBIR-ENQOIR 2009, PAIS 2009, Revised selected papers. Lecture Notes in Computer Science (Vol. 5731, pp. 100–112). Suzhou: Springer. April 2–4, 2009. doi: 10.1007/978-3-642-03996-6_10.
  72. Stefanowski, J., & Weiss, D. (2003). Carrot and language properties in web search results clustering. In E. M. Ruiz, J. Segovia & P. S. Szczepaniak (Eds.). Proceedings of the web intelligence, first international Atlantic web intelligence conference (AWIC’2003). Lecture Notes in Computer Science (Vol. 2663, pp. 240–249). Madrid: Springer. May 5–6, 2003. doi: 10.1007/3-540-44831-4_25.
  73. Tan, P., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Boston: Addison-Wesley.Google Scholar
  74. Toms, E. G. (2000). Serendipitous information retrieval. In DELOS workshop: Information seeking, searching and querying in digital libraries.
  75. Tong, H., & Faloutsos, C. (2006). Center-piece subgraphs: Problem definition and fast solutions. In T. Eliassi-Rad, L. H. Ungar, M. Craven & D. Gunopulos (Eds.). Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 404–413). Philadelphia, PA: ACM. August 20–23, 2006. doi: 10.1145/1150402.1150448.
  76. Tran, Q. T., Chan, C., & Wang, G. (2011). Evaluation of set-based queries with aggregation constraints. In C. Macdonald, I. Ounis & I. Ruthven (Eds.). Proceedings of the 20th ACM conference on Information and knowledge management (CIKM’2011) (pp. 1495–1504). Glasgow: ACM. October 24–28, 2011. doi: 10.1145/2063576.2063791.
  77. Walther, M., & Kaisser, M. (2013). Geo-spatial event detection in the twitter stream. In P. Serdyukov, P. Braslavski, S. O. Kuznetsov, J. Kamps, S. M. Rüger & E. Agichtein et al. (Eds.). Advances in information retrieval: 35th European conference on IR research (ECIR’2013). Proceedings of lecture notes in computer science (Vol. 7814, pp. 356–367). Moscow: Springer. March 24–27, 2013. doi: 10.1007/978-3-642-36973-5_30.
  78. Webb, G. I., Liu, B., Zhang, C., Gunopulos, D., & Wu, X. (Eds.). (2010). In The 10th IEEE international conference on data mining (ICDM’2010). Sydney: IEEE Computer Society. December 14–17, 2010.
  79. White, R. W., Marchionini, G., & Muresan, G. (2008). Evaluating exploratory search systems: Introduction to special topic issue of information processing and management. Informaion Processing and Management, 44(2), 433–436. doi: 10.1016/j.ipm.2007.09.011.CrossRefGoogle Scholar
  80. White, R. W., & Roth, R. A. (2009). Exploratory search: Beyond the query-response paradigm. Synthesis lectures on information concepts, retrieval, and services. Morgan & Claypool Publishers. doi: 10.2200/S00174ED1V01Y200901ICR003.
  81. Wildemuth, B. M., & Freund, L. (2012). Assigning search tasks designed to elicit exploratory search behaviors. In Human–computer information retrieval symposium (HCIR’2012) (p. 4). Cambridge, MA: ACM. October 4–5, 2012. doi: 10.1145/2391224.2391228.
  82. Wilson, M. L., Kules, B., Schraefel, M. C., & Shneiderman, B. (2010). From keyword search to exploration: Designing future search interfaces for the web. Foundations and Trends in Web Science, 2(1), 1–97. doi: 10.1561/1800000003.CrossRefzbMATHGoogle Scholar
  83. Wu, Y. B., Shankar, L., & Chen, X. (2003). Finding more useful information faster from web search results. In Proceedings of the 2003 ACM CIKM international conference on information and knowledge management (pp. 568–571). New Orleans, LA: ACM. November 2–8, 2003. doi: 10.1145/956863.956975.
  84. Yee, K., Swearingen, K., Li, K., & Hearst, M. A. (2003). Faceted metadata for image search and browsing. In G. Cockton & P. Korhonen (Eds.). Proceedings of the 2003 conference on human factors in computing Systems (CHI’2003) (pp. 401–408). Ft. Lauderdale, FL: ACM. April 5–10, 2003. doi: 10.1145/642611.642681.
  85. Yogev, S., Roitman, H., Carmel, D., & Zwerdling, N. (2012). Towards expressive exploratory search over entity-relationship data. In A. Mille, F. L. Gandon, J. Misselis, M. Rabinovich & S. Staab (Eds.). Proceedings of the 21st world wide web conference (WWW’2012) (pp. 83–92). Lyon: ACM. April 16–20, 2012 (Companion Volume). doi: 10.1145/2187980.2187990.
  86. Yue, Z., Han, S., & He, D. (2012). An investigation of search processes in collaborative exploratory web search. Proceedings of the American Society for Information Science and Technology, 49(1), 1–4.CrossRefGoogle Scholar
  87. Zhou, Y., Nie, L., Rouhani-Kalleh, O., Vasile, F., & Gaffney, S. (2010) Resolving surface forms to wikipedia topics. In C. Huang & D. Jurafsky (Eds.). Proceedings of the 23rd international conference on computational linguistics (COLING’2010) (pp. 1335–1343). Beijing: Tsinghua University Press. August 23–27, 2010.

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Unicredit, R&DRomeItaly
  2. 2.Yahoo LabsLondonUK
  3. 3.Qatar Computing Research InstituteDohaQatar
  4. 4.Blueshift LabsSan FranciscoUSA

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