A Printing Workflow Recommendation Tool--Exploiting Correlations between Highly Sparse Case Logs

Abstract

As a user preference prediction mechanism, recommendation techniques have been widely used to support personalized information filtering in current e-commerce applications. We build a recommendation tool into the existing Xerox printing workflow configuration system in order to provide new users with a number of solutions that are possibly of their interests. Such solution recommendations can significantly improve the system efficiency and accuracy by reducing workflow generation overhead and helping users quickly identify their needs. In our work, the main challenge is the high sparsity inherent to our application data - most fields have missing values due to a customer's lack of background or uncertainty on their specific needs. We address this problem by using latent semantic indexing (LSI) to merge original sparse data records into dense and semantic records. The generated dense data are then grouped into clusters based on their correlations. These clusters, together with their user patterns and representative workflows, are used to support efficient online workflow recommendation. Our implemented tool is able to achieve 83% accuracy on a dataset of 4569 case logs with 91% average sparseness