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
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Consumers have short attention spans and eCommerce engines need to have the ability to keep high levels of engagement. In addition, eCommerce stores also need to deal with the issues of cart abandonment and out of stock items, which generally drive engagement down.
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.