User and task analysis for interface design
User and task analysis for interface design
On the recommending of citations for research papers
CSCW '02 Proceedings of the 2002 ACM conference on Computer supported cooperative work
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
Don't look stupid: avoiding pitfalls when recommending research papers
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Information Processing and Management: an International Journal
International Journal of Learning Technology
Mood and Recommendations: On Non-cognitive Mood Inducers for High Quality Recommendation
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
Who predicts better?: results from an online study comparing humans and an online recommender system
Proceedings of the 2008 ACM conference on Recommender systems
UTA-Rec: a recommender system based on multiple criteria analysis
Proceedings of the 2008 ACM conference on Recommender systems
Lessons on applying automated recommender systems to information-seeking tasks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Evaluating Interface Variants on Personality Acquisition for Recommender Systems
UMAP '09 Proceedings of the 17th International Conference on User Modeling, Adaptation, and Personalization: formerly UM and AH
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Automatically building research reading lists
Proceedings of the fourth ACM conference on Recommender systems
Introverted elves & conscientious gnomes: the expression of personality in world of warcraft
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning with personalized recommender systems: A psychological view
Computers in Human Behavior
The evaluation of adaptive and personalised information retrieval systems: a review
International Journal of Knowledge and Web Intelligence
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Recommender systems: from algorithms to user experience
User Modeling and User-Adapted Interaction
Proceedings of the 15th International Conference on Extending Database Technology
Evaluating recommender systems from the user's perspective: survey of the state of the art
User Modeling and User-Adapted Interaction
Explaining the user experience of recommender systems
User Modeling and User-Adapted Interaction
Evaluating the effectiveness of explanations for recommender systems
User Modeling and User-Adapted Interaction
Bisociative music discovery and recommendation
Bisociative Knowledge Discovery
When recommenders fail: predicting recommender failure for algorithm selection and combination
Proceedings of the sixth ACM conference on Recommender systems
A Mobile Service Recommendation System Using Multi-Criteria Ratings
International Journal of Interdisciplinary Telecommunications and Networking
Expert Systems with Applications: An International Journal
Towards a user experience design framework for adaptive spoken dialogue in automotive contexts
Proceedings of the 19th international conference on Intelligent User Interfaces
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Recommender systems do not always generate good recommendations for users. In order to improve recommender quality, we argue that recommenders need a deeper understanding of users and their information seeking tasks. Human-Recommender Interaction (HRI) provides a framework and a methodology for understanding users, their tasks, and recommender algorithms using a common language. Further, by using an analytic process model, HRI becomes not only descriptive, but also constructive. It can help with the design and structure of a recommender system, and it can act as a bridge between user information seeking tasks and recommender algorithms.