Sat, 26 Jul 2008 06:00:53 BST CiteULike: brusilovsky's academic-reference http://www.citeulike.org/user/brusilovsky/tag/academic-reference CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/brusilovsky/article/2682613 <i>(2007), pp. 417-418.</i> Recommending related papers based on digital library access records Stefan Pohl Filip Radlinski Thorsten Joachims doi:10.1145/1255175.1255260 (2007), pp. 417-418. 2008-04-17T15:53:00-00:00 2007 417 418 ACM academic-reference collaborative-filtering dlpaws log-mining http://www.citeulike.org/user/brusilovsky/article/1738453 <i>(2007), pp. 209-216.</i><br /><br />We present a study of the degree to which annotations overlap when several researchers read the same set of scientific articles. Our objective is to determine whether there is sufficient evidence to suggest that information about which passages initial readers tend to annotate might be used to recommend important passages to later readers of the same material. We found that readers exhibit a high degree of overlap in the passages they annotate, that these passages account for a small but significant fraction of the total document, and that such passages are distributed throughout a document rather than concentrated in the same few sections in each paper (e.g., the results section). These findings indicate that work on developing a passage recommendation model based on annotation is warranted. Annotation consensus: implications for passage recommendation in scientific literature Shannon Bradshaw Marc Light doi:10.1145/1286240.1286300 (2007), pp. 209-216. 2007-10-08T03:16:04-00:00 2007 209 216 ACM Press academic-reference annotation http://www.citeulike.org/user/brusilovsky/article/1752789 <i>(2007), pp. 199-208.</i> ASSIST: adaptive social support for information space traversal Rosta Farzan Maurice Coyle Jill Freyne Peter Brusilovsky Barry Smyth doi:10.1145/1286240.1286299 (2007), pp. 199-208. 2007-10-11T00:16:44-00:00 2007 199 208 ACM Press academic-reference social-navigation social-search http://www.citeulike.org/user/brusilovsky/article/618204 <i>(2004), pp. 571-580.</i><br /><br />Today's university library has many digitally accessible resources, both indexes to content and considerable original content. Using off-the-shelf search technology provides a single point of access into library resources, but we have found that such full-text indexing technology is not entirely satisfactory for library searching. In response to this, we report initial usage results from a prototype of an entirely new type of search engine - The System for Electronic Recommendation Filtering (SERF) - that we have designed and deployed for the Oregon State University (OSU) Libraries. SERF encourages users to enter longer and more informative queries, and collects ratings from users as to whether search results meet their information need or not. These ratings are used to make recommendations to later users with similar needs. Over time, SERF learns from the users what documents are valuable for what information needs. In this paper, we focus on understanding whether such recommendations can increase other users' search efficiency and effectiveness in library website searching. Based on examination of three months of usage as an alternative search interface available to all users of the Oregon State University Libraries website (http://osulibrary.oregonstate.edu/), we found strong evidence that the recommendations with human evaluation could increase the efficiency as well as effectiveness of the library website search process. Those users who received recommendations needed to examine fewer results, and recommended documents were rated much higher than documents returned by a traditional search engine. SERF: integrating human recommendations with search Seikyung Jung Kevin Harris Janet Webster Jonathan Herlocker doi:10.1145/1031171.1031277 (2004), pp. 571-580. 2006-05-08T20:23:50-00:00 2004 571 580 ACM Press academic-reference en recommender social-search http://www.citeulike.org/user/brusilovsky/article/1123052 <i>(2006), pp. 192-195.</i> ReMarkables: A Web-Based Research Collaboration Support System Using Social Bookmarking Tools Takashi Ishikawa Piyanuch Klaisubun Masahiro Honma Zhengyu Qian doi:10.1109/WI-IATW.2006.109 (2006), pp. 192-195. 2007-02-26T14:40:53-00:00 2006 192 195 IEEE Computer Society academic-reference bookmarking en tagging http://www.citeulike.org/user/brusilovsky/article/1123094 <i>(2006), pp. 212-215.</i> AcadeMix Juice- A Hybrid Web 2.0/Semantic Web Platform for Exchange of Academic Knowledge Yuri Tijerino Hirohisa Masaki Nobuku Igaki doi:10.1109/WI-IATW.2006.26 (2006), pp. 212-215. 2007-02-26T15:17:40-00:00 2006 212 215 IEEE Computer Society academic-reference en tagging http://www.citeulike.org/user/brusilovsky/article/1014195 <i>(1999)</i><br /><br />The problem of assigning conference paper submissions to suitable reviewers can be viewed as a variant of the general problem of technical paper recommendation. In both cases one would ideally like to direct only those papers that are of the greatest interest to the appropriate set of people. Current attempts to automate the conference reviewing process have typically converted it into a task that requires reviewers to rate keywords and sift through long lists of abstracts to find those that... Recommending papers by mining the web C Basu H Hirsh W Cohen C Nevillmanning (1999) 2006-12-26T06:07:04-00:00 1999 academic-reference recommender http://www.citeulike.org/user/brusilovsky/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 http://www.citeulike.org/user/brusilovsky/article/816963 <i>(2004), pp. 228-236.</i> Enhancing digital libraries with TechLens+ Roberto Torres Sean Mcnee Mara Abel Joseph Konstan John Riedl doi:10.1145/996350.996402 (2004), pp. 228-236. 2006-08-25T21:09:58-00:00 2004 228 236 ACM Press academic-reference recommender http://www.citeulike.org/user/brusilovsky/article/1266 <i>(2004), pp. 553-561.</i> CiteSeer-API: towards seamless resource location and interlinking for digital libraries Yves Petinot Lee Giles Vivek Bhatnagar Pradeep Teregowda Hui Han Isaac Councill doi:10.1145/1031171.1031275 (2004), pp. 553-561. 2004-12-01T16:37:07-00:00 2004 553 561 ACM Press academic-reference information-retrieval http://www.citeulike.org/user/brusilovsky/article/711986 <i>(2002), pp. 116-125.</i> On the recommending of citations for research papers Sean Mcnee Istvan Albert Dan Cosley Prateep Gopalkrishnan Shyong Lam Al Rashid Joseph Konstan John Riedl doi:10.1145/587078.587096< (2002), pp. 116-125. 2006-06-27T00:52:26-00:00 2002 116 125 ACM Press academic-reference recommender http://www.citeulike.org/user/brusilovsky/article/494308 <i>(1998), pp. 89-98.</i> CiteSeer: an automatic citation indexing system Lee Giles Kurt Bollacker Steve Lawrence doi:10.1145/276675.276685 (1998), pp. 89-98. 2006-02-06T17:46:36-00:00 1998 89 98 ACM Press academic-reference information-retrieval http://www.citeulike.org/user/brusilovsky/article/1086243 <i>(2007), pp. 52-61.</i> Collecting community wisdom: integrating social search & social navigation Jill Freyne Rosta Farzan Peter Brusilovsky Barry Smyth Maurice Coyle doi:10.1145/1216295.1216312 (2007), pp. 52-61. 2007-02-03T17:46:15-00:00 2007 52 61 ACM Press academic-reference community social-navigation social-search http://www.citeulike.org/user/brusilovsky/article/374169 <i>Proceedings of the 2002 ACM conference on Computer supported cooperative work (2002), pp. 116-125.</i><br /><br />Collaborative filtering has proven to be valuable for recommending items in many different domains. In this paper, we explore the use of collaborative filtering to recommend research papers, using the citation web between papers to create the ratings matrix. Specifically, we tested the ability of collaborative filtering to recommend citations that would be suitable additional references for a target research paper. We investigated six algorithms for selecting citations, evaluating them through offline experiments against a database of over 186,000 research papers contained in ResearchIndex. We also performed an online experiment with over 120 users to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. We found large differences in the accuracy of the algorithms in the offline experiment, especially when balanced for coverage. In the online experiment, users felt they received quality recommendations, and were enthusiastic about the idea of receiving recommendations in this domain. On the Recommending of Citations for Research Papers Sean Mcnee Istvan Albert Dan Cosley Prateep Gopalkrishnan Shyong Lam Al Rashid Joseph Konstan John Ried Proceedings of the 2002 ACM conference on Computer supported cooperative work (2002), pp. 116-125. 2005-11-01T00:11:45-00:00 2002 Proceedings of the 2002 ACM conference on Computer supported cooperative work 116 125 academic-reference digital-library personalization recommender http://www.citeulike.org/user/brusilovsky/article/375907 <i>International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries (2004), pp. 228-236.</i><br /><br />The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102,000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently. These results can be applied to develop recommender systems for other types of digital libraries. Enhancing Digital Libraries with TechLens+ Roberto Torres Sean Mcnee Mara Abel Joseph Konstan John Riedl International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries (2004), pp. 228-236. 2005-11-01T21:29:27-00:00 2004 International Conference on Digital Libraries,Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries 228 236 academic-reference digital-library personalization recommender http://www.citeulike.org/user/brusilovsky/article/384391 <i>Vol. 3652 (2005), pp. 479-480.</i><br /><br />Managing bibliographic data is a requirement for many researchers. The ShaRef system has been designed to fill the gap between public libraries and personal bibliographies, and provides an open platform for sharing bibliographic data among user groups. Management and Sharing of Bibliographies Erik Wilde Sai Anand Petra Zimmermann doi:10.1007/11551362_44 Vol. 3652 (2005), pp. 479-480. 2005-11-09T01:51:31-00:00 2005 3652 479 480 Springer academic-reference recommender social-navigation http://www.citeulike.org/user/brusilovsky/article/685349 <i>(2001)</i><br /><br />The growing need to manage and exploit the proliferation of online data sources is opening Technical paper recommendation: A study in combining multiple information sources C Basu H Hirsh W Cohen Nevill Manning (2001) 2006-06-05T22:55:17-00:00 2001 academic-reference recommender