Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Modern Information Retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Dynamic Aggregation with Circular Visual Designs
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Introduction to the special section on recommender systems
ACM Transactions on Computer-Human Interaction (TOCHI)
PeerChooser: visual interactive recommendation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Tagsplanations: explaining recommendations using tags
Proceedings of the 14th international conference on Intelligent user interfaces
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Signpost from the masses: learning effects in an exploratory social tag search browser
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering for social tagging systems: an experiment with CiteULike
Proceedings of the third ACM conference on Recommender systems
Content-based recommendation systems
The adaptive web
Who is talking about what: social map-based recommendation for content-centric social websites
Proceedings of the fourth ACM conference on Recommender systems
Evaluating the dynamic properties of recommendation algorithms
Proceedings of the fourth ACM conference on Recommender systems
SFViz: interest-based friends exploration and recommendation in social networks
Proceedings of the 2011 Visual Information Communication - International Symposium
Each to his own: how different users call for different interaction methods in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
TasteWeights: a visual interactive hybrid recommender system
Proceedings of the sixth ACM conference on Recommender systems
Inspectability and control in social recommenders
Proceedings of the sixth ACM conference on Recommender systems
Smallworlds: visualizing social recommendations
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
A field study of a visual controllable talk recommender
Proceedings of the 2013 Chilean Conference on Human - Computer Interaction
See what you want to see: visual user-driven approach for hybrid recommendation
Proceedings of the 19th international conference on Intelligent User Interfaces
Hi-index | 0.00 |
Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities -- i.e. items bookmarked by users, recommendations and tags.