An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Detecting Profile Injection Attacks in Collaborative Recommender Systems
CEC-EEE '06 Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services
Scouts, promoters, and connectors: The roles of ratings in nearest-neighbor collaborative filtering
ACM Transactions on the Web (TWEB)
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal collaborative filtering with adaptive neighbourhoods
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A spatio-temporal approach to collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
Experience Discovery: hybrid recommendation of student activities using social network data
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit
Proceedings of the fifth ACM conference on Recommender systems
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
Using control theory for stable and efficient recommender systems
Proceedings of the 21st international conference on World Wide Web
Adapting to natural rating acquisition with combined active learning strategies
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
Visualizing recommendations to support exploration, transparency and controllability
Proceedings of the 2013 international conference on Intelligent user interfaces
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Collaborative recommendation algorithms are typically evaluated on a static matrix of user rating data. However, when users experience a recommender system, it is dynamic, constantly evolving as new items and new users arrive. The dynamic properties of collaborative recommendation have become important as prediction algorithms based on the interactions of rating histories have been proposed, and as researchers seek to understand problems of robustness and maintenance in rating databases. This paper proposes a new evaluation method for the dynamic aspects of collaborative algorithms, the "temporal leave-one-out" approach, which can provide insight into both user-specific and system-level evolution of recommendation behavior. As a case study, the methodology is applied to the Influence Limiter algorithm [12], showing that its robustness to attack comes at a high accuracy cost.