Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Putting the collaborator back into collaborative filtering
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Online evolutionary collaborative filtering
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
Simple time-biased KNN-based recommendations
Proceedings of the Workshop on Context-Aware Movie Recommendation
Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
Proceedings of the Workshop on Context-Aware Movie Recommendation
Temporal defenses for robust recommendations
PSDML'10 Proceedings of the international ECML/PKDD conference on Privacy and security issues in data mining and machine learning
Performance prediction in recommender systems
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Predicting correctness of problem solving in ITS with a temporal collaborative filtering approach
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Collaborative Filtering Recommender Systems
Foundations and Trends in Human-Computer Interaction
A recommendation model for handling dynamics in user profile
ICDCIT'12 Proceedings of the 8th international conference on Distributed Computing and Internet Technology
Local implicit feedback mining for music recommendation
Proceedings of the sixth ACM conference on Recommender systems
From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews
Proceedings of the 22nd international conference on World Wide Web
Understanding temporal dynamics of ratings in the book recommendation scenario
Proceedings of the 2013 International Conference on Information Systems and Design of Communication
Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols
User Modeling and User-Adapted Interaction
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Collaborative Filtering aims to predict user tastes, by minimising the mean error produced when predicting hidden user ratings. The aim of a deployed recommender system is to iteratively predict users' preferences over a dynamic, growing dataset, and system administrators are confronted with the problem of having to continuously tune the parameters calibrating their CF algorithm. In this work, we formalise CF as a time-dependent, iterative prediction problem. We then perform a temporal analysis of the Netflix dataset, and evaluate the temporal performance of two CF algorithms. We show that, due to the dynamic nature of the data, certain prediction methods that improve prediction accuracy on the Netflix probe set do not show similar improvements over a set of iterative train-test experiments with growing data. We then address the problem of parameter selection and update, and propose a method to automatically assign and update per-user neighbourhood sizes that (on the temporal scale) outperforms setting global parameters.