Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Analysis and detection of segment-focused attacks against collaborative recommendation
WebKDD'05 Proceedings of the 7th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
Collaborative error-reflected models for cold-start recommender systems
Decision Support Systems
BlurMe: inferring and obfuscating user gender based on ratings
Proceedings of the sixth ACM conference on Recommender systems
Stability of Recommendation Algorithms
ACM Transactions on Information Systems (TOIS)
Towards a user based recommendation strategy for digital ecosystems
Knowledge-Based Systems
Knowledge-Based Systems
A Multimedia Recommender System
ACM Transactions on Internet Technology (TOIT)
Defending recommender systems by influence analysis
Information Retrieval
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The paper introduces stability as a new measure of the recommender systems performance. In general, we define a recommendation algorithm to be "stable" if its predictions for the same items are consistent over a period of time, assuming that any new ratings that have been submitted to the recommender system over the same period of time are in complete agreement with system's prior predictions. In this paper, we advocate that stability should be a desired property of recommendation algorithms, because unstable recommendations can lead to user confusion and, therefore, reduce trust in recommender systems. Furthermore, we empirically evaluate stability of several popular recommendation algorithms. Our results suggest that model-based recommendation techniques demonstrate higher stability than memory-based collaborative filtering heuristics. We also find that the stability measure for recommendation techniques is influenced by many factors, including the sparsity of the initial rating data, the number of new incoming ratings (representing the length of the time period over which the stability is being measured), the distribution of the newly added rating values, and the rating normalization procedures employed by the recommendation algorithms.