Collaboration, Reputation and Recommender Systems in Social Web Search

  • Barry Smyth
  • Maurice Coyle
  • Peter Briggs
  • Kevin McNally
  • Michael P. O’Mahony

Abstract

Modern web search engines have come to dominate how millions of people find the information that they are looking for online. While the sheer scale and success of the leading search engines is a testimony to the scientific and engineering progress that has been made over the last two decades, mainstream search is not without its challenges. Mainstream search engines continue to provide a largely one-size-fits-all service to their user-base, ultimately limiting the relevance of their result-lists. And they have only very recently begun to consider how the rise of the social web may support novel approaches to search and discovery, or how such signals can be used to inform relevance. In this chapter we will explore recent research which aims to do just that: to make web search a more personal and collaborative experience and to leverage important information such as the reputation of searchers during result-ranking. In short we look towards a more social future for mainstream search.

References

  1. 1.
    Amershi, S., Morris, M.R.: CoSearch: A System for Co-Located Collaborative Web Search. In: Proceedings of the Annual SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1647–1656 (2008). Florence, ItalyGoogle Scholar
  2. 2.
    Asnicar, F.A., Tasso, C.: IfWeb: A Prototype of User Model-Based Intelligent Agent for Document Filtering and Navigation in the World Wide Web. In: Proceedings of the Workshop on Adaptive Systems and User Modeling on the World Wide Web at the Proceedings of the Annual SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 3–11 (1997). Chia Laguna, SardiniaGoogle Scholar
  3. 3.
    Baeza-Yates, R.A., Hurtado, C.A., Mendoza, M.: Query Recommendation Using Query Logs in Search Engines. In: Current Trends in Database Technology - EDBT 2004 Workshops, pp. 588–596 (2004). Heraklion, GreeceGoogle Scholar
  4. 4.
    Baeza-Yates, R.A., Ribeiro-Neto, B.A.: Modern Information Retrieval. ACM Press / Addison-Wesley (1999)Google Scholar
  5. 5.
    Balabanovic, M., Shoham, Y.: FAB: Content-Based Collaborative Recommender. Communications of the ACM 40(3), 66–72 (1997)CrossRefGoogle Scholar
  6. 6.
    Balfe, E., Smyth, B.: Improving Web Search through Collaborative Query Recommendation. In: Proceedings of the European Conference on Artificial Intelligence (ECAI), pp. 268–272 (2004)Google Scholar
  7. 7.
    Belém, F., Martins, E.F., Almeida, J.M., Gonçalves, M.A.: Exploiting novelty and diversity in tag recommendation. In: ECIR, pp. 380–391 (2013)Google Scholar
  8. 8.
    Billsus, D., Pazzani, M.J., Chen, J.: A learning agent for wireless news access. In: Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI), pp. 33–36 (2000). DOI http://doi.acm.org/10.1145/325737.325768. New Orleans, Louisiana, United States
  9. 9.
    Boydell, O., Smyth, B.: Enhancing case-based, collaborative web search. In: Proceedings of International Conference on Case-Based Reasoning (ICCBR), pp. 329–343 (2007)Google Scholar
  10. 10.
    Bridge, D., Kelly, J.P.: Diversity-enhanced conversational collaborative recommendations. In: N. Creaney (ed.) Procs. of the Sixteenth Irish Conference on Artificial Intelligence and Cognitive Science, pp. 29–38 (2005)Google Scholar
  11. 11.
    Briggs, P., Smyth, B.: Provenance, Trust, and Sharing in Peer-to-Peer Case-Based Web Search. In: Proceedings of European Conference on Case-Based Reasoning (ECCBR), pp. 89–103 (2008)Google Scholar
  12. 12.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30(1–7), 107–117 (1998)Google Scholar
  13. 13.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998). DOI http://dx.doi.org/10.1016/S0169-7552(98)00110-X
  14. 14.
    Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the 7th International Conference on World Wide Web (WWW ’98), pp. 107–117. ACM, Brisbane, Australia (1998)Google Scholar
  15. 15.
    Budzik, J., Hammond, K.J.: User interactions with everyday applications as context for just-in-time information access. In: Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI), pp. 44–51 (2000). New Orleans, Louisiana, United StatesGoogle Scholar
  16. 16.
    Burke, R.: The Wasabi Personal Shopper: A Case-Based Recommender System. In: Proceedings of the 17th National Conference on Artificial Intelligence (AAAI) (1999). Orlando, Florida, USAGoogle Scholar
  17. 17.
    Carroll, J.M., Rosson, M.B.: Paradox of the active user. In: J.M. Carroll (ed.) Interfacing Thought: Cognitive Aspects of Human-Computer Interaction, chap. 5, pp. 80–111. Bradford Books/MIT Press (1987). URL citeseer.ist.psu.edu/carroll87paradox.htmlGoogle Scholar
  18. 18.
    Chakrabarti, S., Dom, B., Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A., Gibson, D., Kleinberg, J.M.: Mining the Web’s Link Structure. IEEE Computer 32(8), 60–67 (1999)CrossRefGoogle Scholar
  19. 19.
    Champin, P.A., Briggs, P., Coyle, M., Smyth, B.: Coping with Noisy Search Experiences. In: 29th SGAI International Conference on Artificial Intelligence (AI), pp. 5–18 (2009). Cambridge, UKGoogle Scholar
  20. 20.
    Chang, H., Cohn, D., McCallum, A.: Learning to Create Customized Authority Lists. In: ICML ’00: Proceedings of the 17th International Conference on Machine Learning (ICML), pp. 127–134 (2000)Google Scholar
  21. 21.
    Chen, L., Pu, P.: Evaluating Critiquing-based Recommender Agents. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI) (2006). Boston, MassachusettsGoogle Scholar
  22. 22.
    Chirita, P.A., Nejdl, W., Paiu, R., Kohlschütter, C.: Using ODP Metadata to Personalize Search. In: Proceedings of the 28th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 178–185 (2005). Salvador, BrazilGoogle Scholar
  23. 23.
    Chirita, P.A., Olmedilla, D., Nejdl, W.: PROS: A Personalized Ranking Platform for Web Search. In: Proceedings of International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH), pp. 34–43 (2004). Eindhoven, The NetherlandsGoogle Scholar
  24. 24.
    Coyle, M., Smyth, B.: Information Recovery and Discovery in Collaborative Web Search. In: Proceedings of the European Conference on Information Retrieval (ECIR), pp. 356–367. Rome, ItalyGoogle Scholar
  25. 25.
    Coyle, M., Smyth, B.: Supporting Intelligent Web Search, volume = 7, year = 2007. ACM Trans. Internet Techn. (4)Google Scholar
  26. 26.
    Croft, W.B., Cronen-Townsend, S., Larvrenko, V.: Relevance Feedback and Personalization: A Language Modeling Perspective. In: DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries (2001). URL citeseer.ist.psu.edu/article/croft01relevance.htmlGoogle Scholar
  27. 27.
    Dahlen, B., Konstan, J., Herlocker, J., Good, N., Borchers, A., Riedl, J.: Jump-Starting MovieLens: User Benefits of Starting a Collaborative Filtering System with “dead-data”. In: University of Minnesota TR 98-017 (1998)Google Scholar
  28. 28.
    Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos, CA (1991)Google Scholar
  29. 29.
    Dolin, R., Agrawal, D., Abbadi, A.E., Dillon, L.: Pharos: A Scalable Distributed Architecture for Locating Heterogeneous Information Sources. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM), pp. 348–355 (1997). Las Vegas, Nevada, United StatesGoogle Scholar
  30. 30.
    Feldman, S., Sherman, C.: The High Cost of Not Finding Information. In: (IDC White Paper). IDC Group (2000)Google Scholar
  31. 31.
    Finkelstein, L., Gabrilovich, E., Matias, Y., Rivlin, E., Solan, Z., Wolfman, G., Ruppin, E.: Placing Search in Context: The Concept Revisited, url = citeseer.ist.psu.edu/finkelstein01placing.html, year = 2001, bdsk-url-1 = citeseer.ist.psu.edu/finkelstein01placing.html. In: Proceedings of the 10th International Conference on the World Wide Web (WWW), pp. 406–414. Hong KongGoogle Scholar
  32. 32.
    Giles, C.L., Bollacker, K.D., Lawrence, S.: CiteSeer: An Automatic Citation Indexing System. In: Proceedings of the 3rd ACM Conference on Digital Libraries (DL), pp. 89–98 (1998). Pittsburgh, Pennsylvania, United StatesGoogle Scholar
  33. 33.
    Granka, L.A., Joachims, T., Gay, G.: Eye-Tracking Analysis of User Behavior in WWW Search. In: Proceedings of the 27th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR), pp. 478–479 (2004). Sheffield, United KingdomGoogle Scholar
  34. 34.
    Gravano, L., García-Molina, H., Tomasic, A.: GlOSS: Text-Source Discovery Over the Internet. ACM Trans. Database Syst. 24(2), 229–264 (1999). DOI http://doi.acm.org/10.1145/320248.320252
  35. 35.
    Hassan, A., White, R.W.: Personalized models of search satisfaction. In: CIKM, pp. 2009–2018 (2013)Google Scholar
  36. 36.
    Hurley, N., Zhang, M.: Novelty and diversity in top-n recommendation - analysis and evaluation. ACM Trans. Internet Techn. 10(4), 14 (2011)CrossRefGoogle Scholar
  37. 37.
    Jameson, A., Smyth, B.: Recommendation to Groups. In: P. Brusilovsky, A. Kobsa, W. Nejdl (eds.) The Adaptive Web, pp. 596–627. Springer-Verlag (2007)Google Scholar
  38. 38.
    Jansen, B.J., Spink, A.: An Analysis of Web Searching by European AlltheWeb.com Users. Inf. Process. Manage. 41(2), 361–381 (2005). DOI http://dx.doi.org/10.1016/S0306-4573(03)00067-0
  39. 39.
    Jansen, B.J., Spink, A., Bateman, J., Saracevic, T.: Real life Information Retrieval: A Study of User Queries on the Web. SIGIR Forum 32(1), 5–17 (1998)CrossRefGoogle Scholar
  40. 40.
    Jeh, G., Widom, J.: Scaling Personalized Web Search. In: Proceedings of the 12th International conference on World Wide Web (WWW), pp. 271–279. ACM (2003). Budapest, HungaryGoogle Scholar
  41. 41.
    Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decision Support Systems 43(2), 618–644 (2007)CrossRefGoogle Scholar
  42. 42.
    Kleinberg, J.M.: Authoritative Sources in a Hyperlinked Environment. J. ACM 46(5), 604–632 (1999). DOI http://doi.acm.org/10.1145/324133.324140
  43. 43.
    Kleinberg, J.M.: Hubs, Authorities, and Communities. ACM Comput. Surv. 31(4), 5 (1999)CrossRefGoogle Scholar
  44. 44.
    Konstan, J., Miller, B., Maltz, D., Herlocker, J., Gorgan, L., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  45. 45.
    Koren, Y.: Tutorial on Recent Progress in Collaborative Filtering. In: Proceedings of the International Conference on Recommender Systems (RecSys), pp. 333–334 (2008)Google Scholar
  46. 46.
    Koutrika, G., Ioannidis, Y.: A Unified User-Profile Framework for Query Disambiguation and Personalization. In: Proc. of the Workshop on New Technologies for Personalized Information Access, pp. 44–53 (2005). Edinburgh, UKGoogle Scholar
  47. 47.
    Kruger, A., Giles, C.L., Coetzee, F.M., Glover, E., Flake, G.W., Lawrence, S., Omlin, C.: DEADLINER: Building a New Niche Search Engine. In: Proceedings of the 9th International Conference on Information and Knowledge Management (CIKM), pp. 272–281. New York, NY, USA (2000). McLean, Virginia, United StatesGoogle Scholar
  48. 48.
    Kuter, U., Golbeck, J.: SUNNY: A new algorithm for trust inference in social networks, using probabilistic confidence models. In: AAAI, pp. 1377–1382 (2007)Google Scholar
  49. 49.
    Lawrence, S., Giles, C.L.: Accessibility of Information on the Web. Nature 400(6740), 107–109 (1999)CrossRefGoogle Scholar
  50. 50.
    Lazzari, M.: An experiment on the weakness of reputation algorithms used in professional social networks: The case of naymz. In: Proceedings of the IADIS International Conference e-Society 2010, pp. 519–522. IADIS, Freiburg, Germany (2010)Google Scholar
  51. 51.
    Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Distributed Systems Online 4(1) (2003)Google Scholar
  52. 52.
    Liu, F., Yu, C., Meng, W.: Personalized Web Search by Mapping User Queries to Categories, year = 2002, bdsk-url-1 = http://doi.acm.org/10.1145/584792.584884. In: Proceedings of the 11th International Conference on Information and Knowledge Management (CIKM), pp. 558–565. ACM Press, New York, NY, USA. DOI http://doi.acm.org/10.1145/584792.584884. McLean, Virginia, USA
  53. 53.
    Ma, Z., Pant, G., Sheng, O.R.L.: Interest-Based Personalized Search. ACM Trans. Inf. Syst. 25(1), 5 (2007). DOI http://doi.acm.org/10.1145/1198296.1198301
  54. 54.
    Makris, C., Panagis, Y., Sakkopoulos, E., Tsakalidis, A.: Category Ranking for Personalized Search. Data Knowl. Eng. 60(1), 109–125 (2007). DOI http://dx.doi.org/10.1016/j.datak.2005.11.006
  55. 55.
    Marchionini, G.: Exploratory search: From Finding to Understanding. Communications of the ACM 49(4), 41–46 (2006). DOI http://doi.acm.org/10.1145/1121949.1121979
  56. 56.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: An analysis of critique diversity in case-based recommendation. In: Proceedings of the International FLAIRS Conference, pp. 123–128 (2005)Google Scholar
  57. 57.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in Dynamic Critiquing. In: Proceedings of the International Conference on Intelligent User Interfaces (IUI), pp. 175–182 (2005)Google Scholar
  58. 58.
    McCarthy, K., Salamó, M., Coyle, L., McGinty, L., Smyth, B., Nixon, P.: Cats: A synchronous approach to collaborative group recommendation. In: Proceedings of the International FLAIRS Conference, pp. 86–91 (2006)Google Scholar
  59. 59.
    McGinty, L., Smyth, B.: Comparison-Based Recommendation. In: S. Craw (ed.) Proceedings of the Sixth European Conference on Case-Based Reasoning (ECCBR 2002), pp. 575–589. Springer (2002). Aberdeen, Scotland.Google Scholar
  60. 60.
    McGinty, L., Smyth, B.: On The Role of Diversity in Conversational Recommender Systems. In: K.D. Ashley, D.G. Bridge (eds.) Proceedings of the 5th International Conference on Case-Based Reasoning, pp. 276–290. Springer-Verlag (2003)Google Scholar
  61. 61.
    McNally, K., O’Mahony, M.P., Coyle, M., Briggs, P., Smyth, B.: A case study of collaboration and reputation in social web search. ACM TIST 3(1), 4 (2011)Google Scholar
  62. 62.
    McNally, K., O’Mahony, M.P., Smyth, B.: A model of collaboration-based reputation for the social web. In: ICWSM (2013)Google Scholar
  63. 63.
    McNally, K., O’Mahony, M.P., Smyth, B., Coyle, M., Briggs, P.: Social and collaborative web search: An evaluation study. In: Proceedings of the 15th International Conference on Intelligent User Interfaces (IUI ’11), pp. 387–390. ACM, Palo Alto, California USA (2011)Google Scholar
  64. 64.
    McNally, K., O’Mahony, M.P., Smyth, B., Coyle, M., Briggs, P.: Social and collaborative web search: an evaluation study. In: IUI, pp. 387–390 (2011)Google Scholar
  65. 65.
    Meuth, R.J., Robinette, P., Wunsch, D.C.: Computational Intelligence Meets the NetFlix Prize. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 686–691 (2008)Google Scholar
  66. 66.
    Micarelli, A., Sciarrone, F.: Anatomy and Empirical Evaluation of an Adaptive Web-Based Information Filtering System. User Modeling and User-Adapted Interaction 14(2–3), 159–200 (2004). DOI http://dx.doi.org/10.1023/B:USER.0000028981.43614.94
  67. 67.
    Mitra, M., Singhal, A., Buckley, C.: Improving Automatic Query Expansion. In: Proceedings of ACM SIGIR, pp. 206–214. ACM Press (1998)Google Scholar
  68. 68.
    Morris, M.R.: A Survey of Collaborative Web Search Practices. In: Proceedings of the Annual SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 1657–1660 (2008)Google Scholar
  69. 69.
    Morris, M.R., Horvitz, E.: SearchTogether: An Interface for Collaborative Web Search. In: UIST, pp. 3–12 (2007)Google Scholar
  70. 70.
    Morris, M.R., Horvitz, E.: S3: Storable, Shareable Search. In: INTERACT (1), pp. 120–123 (2007)Google Scholar
  71. 71.
    Nerur, S.P., Sikora, R., Mangalaraj, G., Balijepally, V.: Assessing the Relative Influence of Journals in a Citation Network. Commun. ACM 48(11), 71–74 (2005)CrossRefGoogle Scholar
  72. 72.
    O’Day, V.L., Jeffries, R.: Orienteering in an Information Landscape: How Information Seekers Get from Here to There. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 438–445. ACM Press, New York, NY, USA (1993). DOI http://doi.acm.org/10.1145/169059.169365. Amsterdam, The Netherlands
  73. 73.
    O’Donovan, J., Evrim, V., Smyth, B.: Personalizing Trust in Online Auctions. In: Proceedings of the European Starting AI Researcher Symposium (STAIRS). Trento, Italy (2006)Google Scholar
  74. 74.
    O’Donovan, J., Smyth, B.: Eliciting Trust Values from Recommendation Errors. In: Proceedings of the International FLAIRS Conference, pp. 289–294 (2005)Google Scholar
  75. 75.
    O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: Proceedings of the International Conference on Intelligent User Interfaces (IUI), pp. 167–174 (2005)Google Scholar
  76. 76.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces (IUI ’05), pp. 167–174. ACM, San Diego, California, USA (2005)Google Scholar
  77. 77.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: IUI, pp. 167–174 (2005)Google Scholar
  78. 78.
    O’Donovan, J., Smyth, B.: Trust No One: Evaluating Trust-based Filtering for Recommenders. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), pp. 1663–1665 (2005)Google Scholar
  79. 79.
    O’Donovan, J., Smyth, B.: Is Trust Robust?: An Analysis of Trust-Based Recommendation. In: Proceedings of the International Conference on Intelligent User Interfaces, pp. 101–108 (2006)Google Scholar
  80. 80.
    O’Donovan, J., Smyth, B.: Mining trust values from recommendation errors. International Journal on Artificial Intelligence Tools 15(6), 945–962 (2006)CrossRefGoogle Scholar
  81. 81.
    Pariser, E.: The Filter Bubble: What the Internet is Hiding from You, year = 2011. Penguin PressGoogle Scholar
  82. 82.
    Pazzani, D.B.M.: A Hybrid User Model for News Story Classification. In: Proceedings of the 7th International Conference on User Modeling (UM) (1999). Banff, CanadaGoogle Scholar
  83. 83.
    Pickens, J., Golovchinsky, G., Shah, C., Qvarfordt, P., Back, M.: Algorithmic Mediation for Collaborative Exploratory Search. In: Proceedings of the International Conference on Information retrieval Research and Development (SIGIR), pp. 315–322 (2008)Google Scholar
  84. 84.
    Preece, J., Shneiderman, B.: The reader to leader framework: Motivating technology-mediated social participation. AIS Trans. on Human-Computer Interaction 1(1), 13–32 (2009)Google Scholar
  85. 85.
    Pretschner, A., Gauch, S.: Ontology Based Personalized Search. In: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), p. 391. IEEE Computer Society, Washington, DC, USA (1999)Google Scholar
  86. 86.
    Rashid, A.M., Ling, K., Tassone, R.D., Resnick, P., Kraut, R., Riedl, J.: Motivating participation by displaying the value of contribution. In: CHI, pp. 955–958 (2006)Google Scholar
  87. 87.
    Reddy, M.C., Dourish, P.: A Finger on the Pulse: Temporal Rhythms and Information Seeking in Medical Work. In: CSCW, pp. 344–353 (2002)Google Scholar
  88. 88.
    Reddy, M.C., Dourish, P., Pratt, W.: Coordinating Heterogeneous Work: Information and Representation in Medical Care. In: CSCW, pp. 239–258 (2001)Google Scholar
  89. 89.
    Reddy, M.C., Jansen, B.J.: A model for understanding collaborative information behavior in context: A study of two healthcare teams. Inf. Process. Manage. 44(1), 256–273 (2008)CrossRefGoogle Scholar
  90. 90.
    Reddy, M.C., Spence, P.R.: Collaborative Information Seeking: A Field Study of a Multidisciplinary Patient Care Team. Inf. Process. Manage. 44(1), 242–255 (2008)CrossRefGoogle Scholar
  91. 91.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic Critiquing. In: P. Funk, P.A.G. Calero (eds.) Proceedings of the 7th European Conference on Case-Based Reasoning, pp. 763–777. Springer-Verlag (2004)Google Scholar
  92. 92.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental critiquing. Knowl.-Based Syst. 18(4–5), 143–151 (2005)CrossRefGoogle Scholar
  93. 93.
    Reilly, J., Smyth, B., McGinty, L., McCarthy, K.: Critiquing with confidence. In: Proceedings of International Conference on Case-Based Reasoning (ICCBR), pp. 436–450 (2005)Google Scholar
  94. 94.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997). DOI http://doi.acm.org/10.1145/245108.245121
  95. 95.
    Resnick, P., Zeckhauser, R.: Trust among strangers in internet transactions: Empirical analysis of ebay’s reputation system. Advances in Applied Microeconomics 11, 127–157 (2002)CrossRefGoogle Scholar
  96. 96.
    van Rijsbergen, C.J.: Information Retrieval. Butterworth (1979)Google Scholar
  97. 97.
    Rocchio, J.: Relevance Feedback in Information Retrieval. G. Salton (editor), The SMART Retrieval System: Experiments in Automatic Document Processing. Prentice–Hall, Inc., Englewood Cliffs, NJ (1971)Google Scholar
  98. 98.
    Rohini, U., Varma, V.: A novel approach for re-ranking of search results using collaborative filtering. In: Proceedings of the International Conference on Computing: Theory and Applications, vol. 00, pp. 491–496. IEEE Computer Society, Los Alamitos, CA, USA (2007). DOI http://doi.ieeecomputersociety.org/10.1109/ICCTA.2007.15
  99. 99.
    Saaya, Z., Rafter, R., Schaal, M., Smyth, B.: The curated web: a recommendation challenge. In: RecSys, pp. 101–104 (2013)Google Scholar
  100. 100.
    Saaya, Z., Schaal, M., Coyle, M., Briggs, P., Smyth, B.: A comparison of machine learning techniques for recommending search experiences in social search. In: SGAI Conf., pp. 195–200 (2012)Google Scholar
  101. 101.
    Saaya, Z., Schaal, M., Coyle, M., Briggs, P., Smyth, B.: Exploiting extended search sessions for recommending search experiences in the social web. In: ICCBR, pp. 369–383 (2012)Google Scholar
  102. 102.
    Saaya, Z., Schaal, M., Rafter, R., Smyth, B.: Recommending topics for web curation. In: UMAP, pp. 242–253 (2013)Google Scholar
  103. 103.
    Sahami, M., Heilman, T.D.: A web-based kernel function for measuring the similarity of short text snippets. In: Proceedings of the International World-Wide Web Conference, pp. 377–386 (2006)Google Scholar
  104. 104.
    Salton, G., Buckley, C.: Improving retrieval performance by relevance feedback. In: Readings in information retrieval, pp. 355–364. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1997)Google Scholar
  105. 105.
    Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: EC ’99: Proceedings of the 1st ACM conference on Electronic commerce, pp. 158–166. ACM Press, New York, NY, USA (1999). DOI http://doi.acm.org/10.1145/336992.337035. Denver, Colorado, United States
  106. 106.
    Shafer, G.: The combination of evidence. International Journal of Intelligent Systems 1(3), 155–179 (1986)MATHCrossRefGoogle Scholar
  107. 107.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings of the Conference on Human Factors in Computing Systems (CHI ’95), pp. 210–217. ACM Press (1995). New York, USAGoogle Scholar
  108. 108.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 210–217 (1995)Google Scholar
  109. 109.
    Shen, X., Tan, B., Zhai, C.: Implicit User Modeling for Personalized Search. In: Proceedings of the Fourteenth ACM Conference on Information and Knowledge Management (CIKM 05) (2005). Bremen, GermanyGoogle Scholar
  110. 110.
    Smeaton, A.F., Foley, C., Byrne, D., Jones, G.J.F.: ibingo mobile collaborative search. In: CIVR, pp. 547–548 (2008)Google Scholar
  111. 111.
    Smeaton, A.F., Lee, H., Foley, C., McGivney, S.: Collaborative video searching on a tabletop. Multimedia Syst. 12(4–5), 375–391 (2007)CrossRefGoogle Scholar
  112. 112.
    Smyth, B.: Case-Based Recommendation. In: P. Brusilovsky, A. Kobsa, W. Nejdl (eds.) The Adaptive Web, pp. 342–376. Springer-Verlag (2007)Google Scholar
  113. 113.
    Smyth, B.: A community-based approach to personalizing web search. IEEE Computer 40(8), 42–50 (2007)CrossRefGoogle Scholar
  114. 114.
    Smyth, B., Balfe, E., Freyne, J., Briggs, P., Coyle, M., Boydell, O.: Exploiting query repetition and regularity in an adaptive community-based web search engine. User Model. User-Adapt. Interact. 14(5), 383–423 (2004)CrossRefGoogle Scholar
  115. 115.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.P.: A case-based perspective on social web search. In: ICCBR ’09: Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development, pp. 494–508. Springer-Verlag, Seattle, Washington, USA (2009)Google Scholar
  116. 116.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.P.: A case-based perspective on social web search. In: Proceedings of International Conference on Case-Based Reasoning (ICCBR) (2009)Google Scholar
  117. 117.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.P.: Google shared. A case study in social search. In: Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP ’09), pp. 283–294. Springer-Verlag, Trento, Italy (2009)Google Scholar
  118. 118.
    Smyth, B., Briggs, P., Coyle, M., O’Mahony, M.P.: Google. Shared! A Case-Study in Social Search. In: Proceedings of the International Conference on User Modeling, Adaptation and Personalization (UMAP), pp. 494–508. Springer-Verlag (2009)Google Scholar
  119. 119.
    Smyth, B., Champin, P.A.: The Experience Web: A Case-Based Reasoning Perspective. In: Workshop on Grand Challenges for Reasoning from Experiences at the International Joint Conference on Artificial Intelligence (IJCAI) (2009)Google Scholar
  120. 120.
    Smyth, B., Coyle, M., Briggs, P.: Heystaks: a real-world deployment of social search. In: RecSys, pp. 289–292 (2012)Google Scholar
  121. 121.
    Smyth, B., McClave, P.: Similarity v’s Diversity. In: D. Aha, I. Watson (eds.) Proceedings of the 3rd International Conference on Case-Based Reasoning, pp. 347–361. Springer (2001)Google Scholar
  122. 122.
    Sparck Jones, K.: A statistical interpretation of term specificity and its application in retrieval. In: Document retrieval systems, pp. 132–142. Taylor Graham Publishing, London, UK, UK (1988)Google Scholar
  123. 123.
    Speretta, M., Gauch, S.: Personalized search based on user search histories. In: WI ’05: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 622–628. IEEE Computer Society, Washington, DC, USA (2005). DOI http://dx.doi.org/10.1109/WI.2005.114
  124. 124.
    Spink, A., Bateman, J., Jansen, M.B.: Searching Heterogeneous Collections on the Web: Behaviour of Excite Users. Information Research: An Electronic Journal 4(2) (1998)Google Scholar
  125. 125.
    Spink, A., Jansen, B.J.: A Study of Web Search Trends. Webology 1(2), 4 (2004). URL http://www.webology.ir/2004/v1n2/a4.html
  126. 126.
    Spink, A., Wolfram, D., Jansen, M.B.J., Saracevic, T.: Searching the Web: the Public and their Queries. Journal of the American Society for Information Science 52(3), 226–234 (2001)CrossRefGoogle Scholar
  127. 127.
    Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive web search based on user profile constructed without any effort from users. In: WWW ’04: Proceedings of the 13th international conference on World Wide Web, pp. 675–684. ACM Press, New York, NY, USA (2004). DOI http://doi.acm.org/10.1145/988672.988764. New York, NY, USA
  128. 128.
    Sun, J.T., Zeng, H.J., Liu, H., Lu, Y., Chen, Z.: Cubesvd: a novel approach to personalized web search. In: WWW ’05: Proceedings of the 14th international conference on World Wide Web, pp. 382–390. ACM Press, New York, NY, USA (2005). DOI http://doi.acm.org/10.1145/1060745.1060803. Chiba, Japan
  129. 129.
    Tan, B., Shen, X., Zhai, C.: Mining long-term search history to improve search accuracy. In: KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 718–723. ACM Press, New York, NY, USA (2006). DOI http://doi.acm.org/10.1145/1150402.1150493. Philadelphia, PA, USA
  130. 130.
    Yang, Y., Chute, C.G.: An example-based mapping method for text categorization and retrieval. ACM Trans. Inf. Syst. 12(3), 252–277 (1994). DOI http://doi.acm.org/10.1145/183422.183424
  131. 131.
    Zhou, B., Hui, S.C., Fong, A.C.M.: An effective approach for periodic web personalization. In: WI ’06: Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence, pp. 284–292. IEEE Computer Society, Washington, DC, USA (2006). DOI http://dx.doi.org/10.1109/WI.2006.36

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Barry Smyth
    • 1
  • Maurice Coyle
    • 2
  • Peter Briggs
    • 2
  • Kevin McNally
    • 1
  • Michael P. O’Mahony
    • 1
  1. 1.Insight Centre for Data AnalyticsUniversity College DublinDublinIreland
  2. 2.HeyStaks Technologies Ltd, NovaUCDUniversity College DublinDublinIreland

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