Case Study:  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
    • Use readily-available multicore hardware
    • Spawn 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 Algorithm
    • Simultaneous clustering of rows and colmns 

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

Case Study Summary

Industry  E-commerce    
Solution  Pervasive DataRush for Analytics
Business Problem Solved  More accurate recommendations to increase sales