OpenTable

“We have thirty million reviews in, in our, kind of, in our database.”

“We sit about sixteen million diners a month”

“Every review that a person leaves, they only allow to leave if they, we know for a fact that they actually dined in that restaurant.”

“From what we looked at so far, it's pretty hard to make that, that extrapolation from that power diner population into the general population, which much more picky about when they would go and why would they go to certain places.”

“For example, New York City, just looked, looked it up before I came to speak here there's a huge trend in cauliflower.”

“We also can teach our restaurants how to market on, on themselves on using words and descriptions that they wouldn't think about otherwise.”

“So, our, literally, it's our most polarizing day on reviews basis, based on one-star review versus five-star reviews.”

“People used to be very generic. People used to say in our reviews, and that's the beauty of the fact that we have 15 years worth of them, people used to say, I just want a booth sorry, people used to say, I just want to be a romantic setting, or I want to have…”

“And so, so that's majority of our revenue comes from the dollar per person, which is, that's how we like it because that is per performance.”

“The model started to get smarter, so the model started to understand things like to die for, crispy, and moist are all positive words, which wouldn't, you wouldn't necessarily understand otherwise.”

“In San Francisco, people talk a lot about the view of the Bay, the view of the bridge. In New York, people talk about, ah, a view of the river, or view of the Hudson, ah, while in Chicago, ah, people talk about the city and the lake.”

“All of our events flowing through Kafka, they've been populated into Cassandra, which then we run Spark instances that kind of model on top of the data.”

“The way restaurants talk about themselves is not always how diners view them. And many times it's actually it's actually interesting that restaurants not even thinking about certain things, that diners who go there express themselves kind of on social media an…”

“It's about 32,000 restaurants, so we provide software that sits inside those restaurants, and allows restaurants to manage their inventory.”

“Non-negative matrix factorization allowed us to break things into just make them much simpler and find topics easier.”