Discrete-time control systems
Modern Control Engineering
IEEE Transactions on Knowledge and Data Engineering
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Multimedia Tools and Applications
Personalized active learning for collaborative filtering
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Online-updating regularized kernel matrix factorization models for large-scale recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
The web changes everything: understanding the dynamics of web content
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Resonance on the web: web dynamics and revisitation patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal diversity in recommender systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Optimizing multiple objectives in 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
Dynamic updating of online recommender systems via feed-forward controllers
Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Spotting trends: the wisdom of the few
Proceedings of the sixth ACM conference on Recommender systems
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The aim of a web-based recommender system is to provide highly accurate and up-to-date recommendations to its users; in practice, it will hope to retain its users over time. However, this raises unique challenges. To achieve complex goals such as keeping the recommender model up-to-date over time, we need to consider a number of external requirements. Generally, these requirements arise from the physical nature of the system, for instance the available computational resources. Ideally, we would like to design a system that does not deviate from the required outcome. Modeling such a system over time requires to describe the internal dynamics as a combination of the underlying recommender model and the its users' behavior. We propose to solve this problem by applying the principles of modern control theory - a powerful set of tools to deal with dynamical systems - to construct and maintain a stable and robust recommender system for dynamically evolving environments. In particular, we introduce a design principle by focusing on the dynamic relationship between the recommender system's performance and the number of new training samples the system requires. This enables us to automate the control other external factors such as the system's update frequency. We show that, by using a Proportional-Integral-Derivative controller, a recommender system is able to automatically and accurately estimate the required input to keep the output close to a pre-defined requirements. Our experiments on a standard rating dataset show that, by using a feedback loop between system performance and training, the trade-off between the effectiveness and efficiency of the system can be well maintained. We close by discussing the widespread applicability of our approach to a variety of scenarios that recommender systems face.