System identification
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Adaptive Control
Hierarchical model-based autonomic control of software systems
DEAS '05 Proceedings of the 2005 workshop on Design and evolution of autonomic application software
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of correlation coefficients on communities of recommenders
Proceedings of the 2008 ACM symposium on Applied computing
Visibility of control in adaptive systems
Proceedings of the 2nd international workshop on Ultra-large-scale software-intensive systems
Scalable adaptive web services
Proceedings of the 2nd international workshop on Systems development in SOA environments
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Software Engineering for Self-Adaptive Systems: A Research Roadmap
Software Engineering for Self-Adaptive Systems
Engineering Self-Adaptive Systems through Feedback Loops
Software Engineering for Self-Adaptive Systems
Putting the collaborator back into collaborative filtering
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Modern Control Theory
Using control theory for stable and efficient recommender systems
Proceedings of the 21st international conference on World Wide Web
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Recommender systems have become an essential software component of many online businesses, supporting customers in finding the items (e.g., books on Amazon, movies on Netflix, songs on Last.fm) they are interested in. Key to their success is the level of accuracy they achieve: the more precisely they can predict how much a customer will enjoy an item, the higher the profit that the business will make (e.g., in terms of more purchases). In quantifying the accuracy of recommender systems, the evaluation methodology followed by researchers has so far neglected an important aspect: that these businesses grow continuously over time, both in terms of users and items. The data structures used by the recommender system to compute predictions become stale and thus have to be updated regularly. Intuitively, the more often the data structures are being updated, the higher the accuracy achieved, but the higher the computational cost afforded, because of the extremely large volume of data being handled. System administrators often perform the update at fixed intervals of time (e.g., weekly, fortnightly), in an effort to balance accuracy versus cost. We argue that such an approach benefits neither accuracy nor cost, as businesses do not grow linearly in time, thus risking the fixed update interval to be at times too coarse (with negative impact on accuracy), and at other times too fine grained (with negative impact on cost). We thus advocate for a self-monitoring and self-adaptive approach, whereby the system monitors its own growth over time, estimates the loss in accuracy it would endure if an update were not being performed based on the observed growth, and dynamically decides whether the benefit of performing an update (accuracy) outweighs its computational cost. Using real data from the Bibsonomy website, we demonstrate how this simple technique enables system administrators to transparently balance these two conflicting requirements.