muRecommend™

A Meta-software that integrates cross-industry experience in the recommendation, personalization and
CRM landscape

The combined strength of individual modules in the framework multiplies effectiveness at scale

Contextual Synergy

  • Recommendations based on given features and Identifying latent features for users and products

  • Ensemble of many recommendation algorithms that handles both variety and similarity

Scalability

  • Low dependency on environment / architecture

  • Fits with any analytical process flow seamlessly

  • Supports big data through python server

Data Driven Class Selection

  • Input data driven algorithm selection

  • Distribution fitting, variance and correlation checks for determining quality of variables

  • Rule based decision tree will determine the algorithms that need to be run based on the above checks

Speed vs Accuracy

  • Full blown factorization algorithms that focus on accuracy

  • Hybrid models with hashing that focus on speed

  • Multiple algorithms enable the user to pick between quasi real time algorithms or accuracy focused algorithms

The framework accounts for three dimensions – Data, Context and Algorithms





  • Content based recommendation

  • Collaborative filtering

  • Hybrid model

  • Association rules


  • “People who are similar have bought this item”

  • “People who have bought this item have also bought the following”

  • Product bundling





  • Transactional

  • Product ranking (Implicit/Explicit)

  • Click stream data

  • Cached data

  • User/Item features data

Back to Top