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
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Recommendations based on given features and Identifying latent features for users and products
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Ensemble of many recommendation algorithms that handles both variety and similarity
Scalability
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Low dependency on environment / architecture
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Fits with any analytical process flow seamlessly
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Supports big data through python server
Data Driven Class Selection
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Input data driven algorithm selection
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Distribution fitting, variance and correlation checks for determining quality of variables
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Rule based decision tree will determine the algorithms that need to be run based on the above checks
Speed vs Accuracy
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Full blown factorization algorithms that focus on accuracy
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Hybrid models with hashing that focus on speed
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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
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Content based recommendation
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Collaborative filtering
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Hybrid model
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Association rules
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“People who are similar have bought this item”
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“People who have bought this item have also bought the following”
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Product bundling
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Transactional
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Product ranking (Implicit/Explicit)
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Click stream data
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Cached data
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User/Item features data