What are Recommendation Engines?
One of the earliest forms of AI solutions were Recommendation Engines. Recommendation Engines analyze large data set accumulated over time to make suggestions for something a user might be interested in, for instance a product or content. Such solutions first became known in the online retail space, essentially in the form of Intelligent Shopping Assistants. Product purchase recommendations have been encountered by almost everyone, “Customers who bought X also bought….”. However, as more business activities and processes evolve into online interactive solutions, the use cases for Recommendation Engines have exploded. Equally, the datasets that these engines need to consume have multiplied in number and volume. This evolution has led us to solutions that can process vast troves of past user activity to produce highly customized recommendations.
How Macrosoft AI Built Our Recommendation Engines
Macrosoft AI Recommendation Engines utilize algorithms that are trained with data that reflects the preferences, interests and observed behavior for all critical Influencers within a given marketplace. Our Recommendation Engines can filter through millions of transactions real time and on a continual basis, allowing for a level of insight that has no theoretical limit.
It is the ability to continually filter through millions of transactions, combined with continuous learning (i.e. incorporating the result of each transaction within the “training dataset”) that provides the power behind Macrosoft AI’s Recommendation Engines.
Collaborative and Content Filtering
We employ two categories of data filtering used to fuel the algorithms behind these solutions. Collaborative Filtering focuses on data related to the profile of a given end user as well as that for the entire user community, allowing for recommendations based on users with similarity within their profiles. Content Filtering, on the other hand, focuses on observed activity by all end users in conjunction with their known preferences. These two filtering approaches can he used in unison in what can be called Hybrid Filtering.
How Macrosoft AI Tackles Data Analysis in Recommendation Engines
While most of the attention is on the AI model & algorithms in use, quite often the most significant challenge is working with the vast troves of data needed to train these solutions. Millions of past transactions, and the databases that house this data, must be evaluated to determine which data is needed for model training. Quite often, the use of third-party datasets is required, which adds to the challenge of collecting the required data to produce an integrated, normalized dataset for model training. Producing the required integrated, and properly labeled training data is often out of reach for many businesses that wish to use AI. Macrosoft AI’s data science heritage allows us to team up Data Experts along with AI Scientists to tackle the full scope of work, reducing or even eliminating the need for end users to participate in data exercises.