From X-rays to silly putty via uranus: Serendipity and its role in web search

Conference on Human Factors in Computing Systems - Proceedings

Volumn , Issue , 2009, Pages 2033-2036

  André, Paul ^a   Teevan, Jaime ^b   Dumais, Susan T ^b  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b MICROSOFT RESEARCH   (United States)

Author keywords

Partially relevant results; Personalization; Serendipity; Web search

Indexed keywords

HIGHLY ACCURATE; PARTIALLY RELEVANT RESULTS; PERSONALIZATIONS; SERENDIPITY; WEB SEARCH QUERIES; WEB SEARCHES;

HUMAN ENGINEERING; SEARCH ENGINES; WEBSITES;

INFORMATION RETRIEVAL;

EID: 70349562168     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1518701.1519009     Document Type: Conference Paper

References (17)

1. De Bruijn, O. & Spence, R. A new framework for theory-based interaction design applied to serendipitous information retrieval, ACMTOCHI 15, 1(2008), 1-38.
2. Erdelez, S. Information encountering: It's more than just bumping into information, Bulletin of the American Society for Information Science 25, 3(1999) 25-29.
3. Erdelez, S. Investigation of information encountering in the controlled research environment, Information Processing and Management 40, 6(2004), 1013-1025.
4. Foster, A. & Ford, N. Serendipity and information seeking: An empirical study, J. Documentation 59, 3 (2003) 321-340.
5. Herlocker, J. L., Konstan, J. A., Terveen, L. G. & Riedl, J. T. Evaluating collaborative filtering recommender systems, ACM TOIS 22, 1 (2004), 5-53.
6. Johnson, S. B. (2006), Can we please kill this meme now. Retrieved 14 Sep 2008: http://tinyurl.com/sbjohnson.
7. McKeen, W. (2006), The endangered joy of serendipity, St. Petersburg Times Retrieved Sep 2008 http://tinyurl.com/mckeen.
8. Ranie, L. & Shermak, J. (2005), Pew Internet and American Life Project: Data memo on search engine use. Retrieved 14 Sep 2008: http://pewinternet.org/pdfs/PIP-SearchData-l105.pdf.
9. Sarwar, B., Karypis, G., Konstan, J. & Reidl, J. Item-based collaborative filtering recommendation algorithms, Proc WWW 01, 285-295.
10. Shen, X., Tan, B. & Zhai, C. Implicit user modeling for personalized search, Proc CIKM'05 824-831.
11. Spink, A., Greisdorf, H. & Bateman, J. From highly relevant to not relevant: Examining different regions of relevance, IP&M 34, 5 (1998), 599-621.
12. Teevan, J., Dumais, S.T. & Horvitz, E. Beyond the commons: on the value of personalizing Web search. Proc PIA '05, 84-92.
13. H.Teevan, J., Dumais, S.T. & Horvitz, E. Personalizing search via automated analysis of interests and activities, Proc SIGIR '05, 449-456.
14. Toms, E. Serendipitous information retrieval, Proc of the First DELOS Network of Excellence Workshop on Information Seeking. Searching and Querying in Digital Libraries, 2000.
15. Van Andel, P. Anatomy of the Unsought Finding. Serendipity: Origin, history, domains, traditions, appearances, patterns and programmability, The British Journal for the Philosophy of Science 45, 2 (1994), 631 -648.
16. Wolfe, M., Schreiner, M., Rehder, Bm, Laham, D., Foltz, P., Kintsch, W. & Landauer, T. Learning from text: Matching readers and texts by Latent Semantic Analysis, Discourse Processes 25, (1998) 309-336.
17. Ziegler, C., McNee, S.M., Konstan, J.A. & Lausen, G. Improving recommendation lists through topic diversification, Proc. WWW '05 22-32.