Don't look stupid: Avoiding pitfalls when recommending research papers

Sean M. McNee, Nishikant Kapoor, Joseph A. Konstan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

52 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th Anniversary ACM Conference on Computer Supported Cooperative Work, CSCW 2006
Pages171-180
Number of pages10
DOIs
StatePublished - Dec 1 2006
Event20th Anniversary ACM Conference on Computer Supported Cooperative Work, CSCW 2006 - Banff, AB, Canada
Duration: Nov 4 2006Nov 8 2006

Publication series

NameProceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Other

Other20th Anniversary ACM Conference on Computer Supported Cooperative Work, CSCW 2006
CountryCanada
CityBanff, AB
Period11/4/0611/8/06

Keywords

  • Collaborative filtering
  • Content-based filtering
  • Digital libraries
  • Human-recommender interaction
  • Information seeking
  • Personalization
  • Recommender systems

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