Using control theory for stable and efficient recommender systems
- 16 April 2012
- conference paper
- conference paper
- Published by Association for Computing Machinery (ACM)
Abstract
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.Keywords
This publication has 19 references indexed in Scilit:
- Dynamic updating of online recommender systems via feed-forward controllersPublished by Association for Computing Machinery (ACM) ,2011
- Evaluating the dynamic properties of recommendation algorithmsPublished by Association for Computing Machinery (ACM) ,2010
- Resonance on the webPublished by Association for Computing Machinery (ACM) ,2009
- The web changes everythingPublished by Association for Computing Machinery (ACM) ,2009
- A new similarity measure for collaborative filtering to alleviate the new user cold-starting problemInformation Sciences, 2008
- Automatic metadata expansion and indirect collaborative filtering for TV program recommendation systemMultimedia Tools and Applications, 2006
- Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensionsIEEE Transactions on Knowledge and Data Engineering, 2005
- Multifractal random walkPhysical Review E, 2001
- Optimum Settings for Automatic ControllersJournal of Dynamic Systems, Measurement, and Control, 1993
- Dynamic Network Traffic Assignment Considered as a Continuous Time Optimal Control ProblemOperations Research, 1989