Yelp Advisor — 2016

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Figuring out where to eat isn’t just time consuming, it’s also inefficient. Between two people, making plans can be a huge hassle, because there are too many steps involved with making a decision. Planning a meet-up occurs in a single conversation, but virtually, it usually involves at least three separate app environments, including messaging, calling, navigating, using Yelp, or more. Furthermore, if those apps don't communicate with each other, there is often overlap in the data entered. With Yelp Advisor, access all the information you need to get from planning to doing by simply having a conversation.

When enabled, Yelp Advisor uses natural language processing to read and parse through conversations happening in real time. An understanding of sentence structure allows Yelp Advisor to determine what information to keep track of in anticipation of a key prompt, at which point a red notification dot indicates that an informed search can be made.

Swipe through Yelp suggestions and change the search parameters if there are implicit filters that weren’t picked up through the conversation (dietary restrictions, kid-friendly, price range). Suggestions are ranked by learned data from both Facebook and Yelp accounts, weighing not just proximity but also similarity to places you’ve been before. If you’re looking for something different, simply tap to load more similarly-chosen suggestions.

Our initial process was to create a way in which natural language processing and machine learning could improve an already existing task from a design perspective. Long before reaching our final stage of a Yelp-powered Facebook Messenger plugin, we studied numerous examples of these processes being used in all types of Internet-based industries. What stood out to us most in comparing the different types of machine learning tasks (from synthesis to summarization to search, etc.) was their common thread: performing actions based on datasets that are too large for humans to parse through. Ultimately, an expert human will understand their own preferences enough to make the decision that is right for them; however, given large amounts of options and potential conclusions, it can be very difficult for anyone to make sense of the information that’s presented to us.

We’ve since been interested in the potential for machine learning systems to help prune these large datasets down into smaller ones that can be easily processed by people. In this way, information becomes more accessible to users, but the users themselves are ultimately still the ones making the decisions.

Yelp Advisor was a collaborative design concept created by Jingru Guo and Jeremy Joachim. Special Thanks to Ivy Hu and ASAPP.