CUERIS

ECOMMERCE RECOMMENDATION ENGINE

ECOMMERCE RECOMMENDATION ENGINE

Business goal:

To provide personalized recommendations based on consumer and socio-economic profile and purchase intelligence to increase up-selling and cross selling opportunities and to design personalized targeting and retargeting campaigns


For more information, contact us

Challenge:

A personalized recommendation engine based on the following machine learning algorithms was developed:

  • Collaborative Filtering – Matrix Factorization
  • Semi Restricted Boltzmann Machines
  • General Factorization Framework – Context-aware recommendations
  • Alternating Least Squares (ALS) Learning

Technology stack: Django, Spark, Kafka, HDFS, Gobblin, MongoDB.