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

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’ alg...

• Type: Conference
• Year: 2006
• Where: CSCW
• Authors: Sean M. McNee, Nishikant Kapoor, Joseph A. Konstan
• Keywords: CSCW 2006, Digital Library, Information Seeking Tasks, Recommender Algorithms, Social Sciences