Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Enhancing digital libraries with TechLens+
Proceedings of the 4th ACM/IEEE-CS joint conference on Digital libraries
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Making recommendations better: an analytic model for human-recommender interaction
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering
Proceedings of the 2007 ACM conference on Recommender systems
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
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Recommending multimedia web services in a multi-device environment
Information Systems
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Automated recommender systems predict user preferences by applying machine learning techniques to data on products, users, and past user preferences for products. Such systems have become increasingly popular in entertainment and e-commerce domains, but have thus far had little success in information-seeking domains such as identifying published research of interest. We report on several recent publications that show how recommenders can be extended to more effectively address information-seeking tasks by expanding the focus from accurate prediction of user preferences to identifying a useful set of items to recommend in response to the user's specific information need. Specific research demonstrates the value of diversity in recommendation lists, shows how users value lists of recommendations as something different from the sum of the individual recommendations within, and presents an analytic model for customizing a recommender to match user information-seeking needs.