System and method for intelligent context-based personalized beauty product recommendation and matching

A system for intelligent context-based personalized beauty product recommendation and matching, comprising:

• a computer system comprising a memory and a processor;
• a data extraction engine, comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, causes the computer system to:
• scrape product data from a plurality of data sources accessible via the internet;
• store the scraped product data as a product vector in the memory;
• receive customer data input responses, the customer data input responses comprising at least a fact-based input, at least a concern-based input, at least a preference-based input, and at least a goal-based input;
• create an individual profile of the customer using the customer data input responses and store the individual profile in the memory;
• and
• map the customer data input responses to product requirements and store the mapped customer data input responses in the individual profile;
• and
• a data analysis and recommendation engine, comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, causes the computer system to:
• retrieve the individual profile from the memory;
• retrieve the fact-based input responses and extract environmental conditions from a locational input included in the fact-based input;
• combine the mapped customer data input responses and the extracted environmental conditions into a requirement vector;
• retrieve one or more product vectors from the memory;
• compute the similarity between the requirement vector and the one or more product vectors;
• identify the product associated with the product vector that has the highest computed similarity score;
• and
• display the identified product as a recommended product to the customer.