Deep learning algorithms power startup’s beauty database

To do that, Proven Beauty uses what Zhao called the “three main pillars of technology.” To collect the data, Yuan, co-founder and CTO at Proven Beauty and a computational physicist from Stanford, built millions of automated bots that continuously crawl and scrape data such as ingredient lists, product names and customer reviews from e-commerce platforms.

Next, Proven Beauty uses semantic search to extract information from customer reviews such as the product name, any skin concerns the customer may have and any data on the customer’s experience with the product. The data is a mix of structured data such as timestamps and product information, as well as unstructured data such as text. To determine the sentiment captured in the text, Proven Beauty relies on natural language processing, that is, a program that’s able to understand language. The data is then used to construct a consumer knowledge graph, which connects what reviewers say about a product to their personal characteristics, according to Zhao.

Advice from CEO Ming Zhao

Zhao’s one piece of advice to CIOs who are planning to invest in artificial intelligence is “to find the best people you can find to get the job done.” Proven Beauty’s competitive differentiator is its beauty database, which took two years to construct. Zhao said it was her co-founder, Yuan, who did the yeoman’s work of building the beauty genome project, often tapping experts at Google Brain and other tech companies to guide their progress.

“Semantic search is doing the same thing with the ingredients of the products that people are talking about,” she said.

Finally, Proven Beauty is combining the consumer and the ingredient knowledge graphs and using machine learning and deep learning algorithms to determine the efficacy of an active ingredient within a product against skin concerns, demographic, geographic and genetic data.

“It’s a pretty difficult problem,” Zhao said. One aspect that makes the problem especially complex is that active ingredients don’t operate within a vacuum. Instead, they require a network of inactive chemicals such as preservatives and other active ingredients to work. To pinpoint and understand the relationships between an active ingredient and a sub-segment of women, Proven Beauty is unleashing deep learning algorithms onto the data, she said.

“It is a major project for what seems to be a more everyday industry,” Zhao said. “But I think that’s exactly what is needed, especially for products that we women care about.”

Based on the information provided from the Proven Beauty quiz, customers are sent a skin profile, which includes a visual breakdown of their skin type, as well as a list of customized skin care products.