GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Computational models of information scent-following in a very large browsable text collection
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
An algorithm for suffix stripping
Readings in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Analysis of a very large web search engine query log
ACM SIGIR Forum
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
ACM Transactions on Information Systems (TOIS)
A Maximum Entropy Approach for Collaborative Filtering
Journal of VLSI Signal Processing Systems
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Meeting user information needs in recommender systems
Meeting user information needs in recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Hybrid, Multi-dimensional Recommender for Journal Articles in a Scientific Digital Library
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Challenges in supporting end-user privacy and security management with social navigation
Proceedings of the 5th Symposium on Usable Privacy and Security
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
CommunityCommands: command recommendations for software applications
Proceedings of the 22nd annual ACM symposium on User interface software and technology
Collaborative filtering for social tagging systems: an experiment with CiteULike
Proceedings of the third ACM conference on Recommender systems
Ontological technologies for user modelling
International Journal of Metadata, Semantics and Ontologies
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
Integrating user feedback with heuristic security and privacy management systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A source independent framework for research paper recommendation
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Fusing Recommendations for Social Bookmarking Web Sites
International Journal of Electronic Commerce
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Smallworlds: visualizing social recommendations
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Combining social information for academic networking
Proceedings of the 2013 conference on Computer supported cooperative work
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
Research paper recommender system evaluation: a quantitative literature survey
Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation
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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.