SWAMI: A Framework for Collaborative Filtering Algorithm Development and Evaluation
Danyel Fisher, Kris Hildrum, Jason Hong, Mark Newman, Megan Thomas, and Rich Vuduc
We present a Java-based framework, SWAMI (Shared Wisdom through the Amalgamation of Many Interpretations) for building and studying collaborative filtering systems. SWAMI consists of three components: a prediction engine, an evaluation system, and a visualization component. The prediction engine provides a common interface for implementing different prediction algorithms. The evaluation system provides a stan-dardized testing methodology and metrics for analyzing the accuracy and run-time performance of prediction algorithms. The visualization component suggests how graphical representations can inform the development and analysis of prediction algorithms. We demonstrate SWAMI on the EachMovie data set by comparing three prediction algorithms: a traditional Pearson correlation-based method, support vector machines, and a new accurate and scalable correlation-based method based on clustering techniques.