Pervasive DataRush Use Case - E-Commerce Recommender System

Problem

• Customer satisfaction and loyalty needs to be improved
• Recommender system data is large and sparse
• Training run time is too long
• Current system doesn't meet accuracy targets

The Pervasive DataRush Solution

• Apply parallelism to Data Mining using Pervasive DataRush
  • Uses commodity multicore hardware
  • Spawns as many threads as available
• Use Pervasive dataflow model for large and sparse data
  • Data intensive without memory limitations
  • Horizontal partitioning based on number of cores
  • Sparse matrix reader with parallel parsing and extracting
• Utilize state of the art Information Theoretic Coclustering Algorighm

Pervasive DataRush Benefits

• Training runtime reduced from days to less than 17 minutes
• Recommendation accuracy increased
• System scalable across cores and data volumes
• Rapid development environment