Dynamic recommendations personalized by historical data

Abstract

Disclosed are various embodiments of systems and methods for dynamically generating and providing personalized recommendations of newer products or services potentially of interest to a particular user who has previously purchased a similar product or service. Historical purchase data or other information indicating the user's preferences is analyzed to determine personal preference data. Candidate content is identified based on attributes found in the preference data. Similarity strategies and criteria can be used to test features and qualities in candidate content. Recommended product or service content comes in the form of candidate content which reaches a similarity threshold or otherwise achieves a sufficient confidence score based at least in part on a similarity metric is determined. In accordance with various embodiments, the generation of newer product and service recommendations can be accomplished or optimized by training machine learning models, such as deep neural networks, using preference data for the particular user.