Thu, 24 Jul 2008 23:15:17 BST CiteULike: Group: Adaptive-Web - with tag empirical-study http://www.citeulike.org/group/2118/tag/empirical-study CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/group/2118/article/2862354 <i>(2008), pp. 1271-1274.</i> Predictability and accuracy in adaptive user interfaces Krzysztof Gajos Katherine Everitt Desney Tan Mary Czerwinski Daniel Weld doi:10.1145/1357054.1357252 (2008), pp. 1271-1274. 2008-06-04T19:28:05-00:00 2008 1271 1274 ACM adaptive-interface empirical-study en http://www.citeulike.org/group/2118/article/2931514 <i>(2008), pp. 1267-1270.</i> Evaluation of a role-based approach for customizing a complex development environment Leah Findlater Joanna Mcgrenere Joanna Mcgrenere David Modjeska David Modjeska doi:10.1145/1357054.1357251 (2008), pp. 1267-1270. 2008-06-26T17:44:52-00:00 2008 1267 1270 ACM adaptability adaptive-interface empirical-study http://www.citeulike.org/group/2118/article/2862084 <i>(2008), pp. 1247-1256.</i> Impact of screen size on performance, awareness, and user satisfaction with adaptive graphical user interfaces Leah Findlater Joanna Mcgrenere doi:10.1145/1357054.1357249 (2008), pp. 1247-1256. 2008-06-04T17:42:28-00:00 2008 1247 1256 ACM adaptive-interface empirical-study http://www.citeulike.org/group/2118/article/1304260 <i>(2006), pp. 11-18.</i> User performance versus precision measures for simple search tasks Andrew Turpin Falk Scholer doi:10.1145/1148170.1148176 (2006), pp. 11-18. 2007-05-17T21:05:40-00:00 2006 11 18 ACM Press empirical-study evaluation information-retrieval http://www.citeulike.org/group/2118/article/1291540 <i>(2007), pp. 581-590.</i> A large-scale evaluation and analysis of personalized search strategies Zhicheng Dou Ruihua Song Ji-Rong Wen doi:10.1145/1242572.1242651 (2007), pp. 581-590. 2007-05-12T17:41:52-00:00 2007 581 590 ACM Press adaptive-search adaptive-web empirical-study personalization http://www.citeulike.org/group/2118/article/1286333 <i>(2007), pp. 21-30.</i> Investigating behavioral variability in web search Ryen White Steven Drucker doi:10.1145/1242572.1242576 (2007), pp. 21-30. 2007-05-09T20:11:37-00:00 2007 21 30 ACM Press empirical-study www-search http://www.citeulike.org/group/2118/article/960041 <i>(2005), pp. 2009-2012.</i> An empirical assessment of adaptation techniques Theophanis Tsandilas Schraefel doi:10.1145/1056808.1057079 (2005), pp. 2009-2012. 2006-11-24T00:48:19-00:00 2005 2009 2012 ACM Press adaptive-interface empirical-study http://www.citeulike.org/group/2118/article/960014 <i>(2006), pp. 201-208.</i> Exploring the design space for adaptive graphical user interfaces Krzysztof Gajos Mary Czerwinski Desney Tan Daniel Weld doi:10.1145/1133265.1133306 (2006), pp. 201-208. 2006-11-23T23:18:02-00:00 2006 201 208 ACM Press adaptive-interface empirical-study en http://www.citeulike.org/group/2118/article/1246118 <i>User Modeling and User-Adapted Interaction, Vol. 13, No. 4. (1 November 2003), pp. 373-396.</i> User Attitudes Regarding a User-Adaptive eCommerce Web Site Sherman Alpert John Karat Clare-Marie Karat Carolyn Brodie John Vergo doi:10.1023/A:1026201108015 User Modeling and User-Adapted Interaction, Vol. 13, No. 4. (1 November 2003), pp. 373-396. 2007-04-23T22:54:56-00:00 2003 User Modeling and User-Adapted Interaction 13 4 373 396 e-commerce empirical-study en open-user-model personalization user-centered-design http://www.citeulike.org/group/2118/article/1007839 <i>(2006), pp. 171-180.</i><br /><br />If recommenders are to help people be more productive, they need to support a wide variety of real-world information seeking tasks, such as those found when seeking research papers in a digital library. There are many potential pitfalls, including not knowing what tasks to support, generating recommendations for the wrong task, or even failing to generate any meaningful recommendations whatsoever. We posit that different recommender algorithms are better suited to certain information seeking tasks. In this work, we perform a detailed user study with over 130 users to understand these differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library. We found that pitfalls are hard to avoid. Two of our algorithms generated 'atypical' recommendations recommendations that were unrelated to their input baskets. Users reacted accordingly, providing strong negative results for these algorithms. Results from our 'typical' algorithms show some qualitative differences, but since users were exposed to two algorithms, the results may be biased. We present a wide variety of results, teasing out differences between algorithms. Finally, we succinctly summarize our most striking results as "Don't Look Stupid" in front of users. Don't look stupid: avoiding pitfalls when recommending research papers Sean Mcnee Nishikant Kapoor Joseph Konstan doi:10.1145/1180875.1180903 (2006), pp. 171-180. 2006-12-22T14:10:04-00:00 2006 171 180 ACM Press academic-reference empirical-study en recommender