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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