Our recommender engine with sophisticated AI at its core can be customized to meet your specifications.
We offer smooth and easy integration with existing information management systems across domains.
OPTIMIZE FROM ENSEMBLE ALGORITHMS
CUSTOMIZE TO YOUR NEEDS
UNLOCK THE POTENTIAL OF YOUR DATA
The recommender engine leverages machine learning algorithms using three principles:
People Like You
Items Like This
State your need and our engine will identify the algorithm that generates the best results for your business and your employees.
SO HOW DOES THIS WORK?
Identification of Agent and Entity
In a matching scenario, the first step is to identify the individual agent that must be recommended the best entity from a catalog. So, in an e-commerce scenario, the agent is the consumer and the entity is the product featured on the site.
Breaking down each agent and each entity and defining their individual attributes through a process of design thinking and close collaboration with all relevant stakeholders allows us to ensure we've identified the right levers for the recommender engine.
Bringing in the Data
The data in the agent and entity profiles undergoes conversion into clean, structured data, which is in turn translated into informative values called features.
Our ensemble approach allows us to optimize the algorithms required to generate the best results, subject to the size and quality of the dataset.
Based on your core problem statement, our machine learning models will assess the use of distance similarity measures (for agent-agent or entity-entity matching) or basket analysis on historical transactions (which offer better agent-entity mining).
For larger datasets, our models can uncover the latent features underlying the interactions between agents and entities to drive more targeted recommendations. But for smaller datasets, we overcome the cold start problem through a process of feature-mapping, driven by collaboration with experts in the industry.