Explore restaurants that your foodie friends like or want to try.
Easily add, manage and look for your favorite restaurants and places on your "to eat" list.
Find restaurants anytime, anywhere, from your or your friends' lists.
Besides the basic information, reviews from other platforms are also available for you to refer to.
How might we help people find restaurants from recommendations
of their friends?
Before jumping into user research, the very first question was, what does this new service really mean? What’s the difference between this service and other existing ways people currently use? After did a little bit of research, I realized that each of the current information sources people use to find restaurants has it’s own “mechanism of trust”. And the mechanism consists of two parts: the origin of data and the type of data.
Why do users use this service when there are already other popular information sources/services existing around?
To answer this question, I interviewed 10 participants between 20–40, all of them are Yelp or google users. Participants were asked to arrange the cards with information sources to show how frequently they use and how much they trust each of them. The 3 major research questions are:
Besides credibility, agility and data efficiency are two other qualities that users expect to see from the information sources.
Agility: how widely an information source applies to various user scenarios. e.g. users will not text their friends for recommendations when they happen to be an unfamiliar neighborhood.
Data efficiency: how much data is needed for one to trust an information source. e.g. a participant said that he barely goes to restaurants with fewer than 50 comments on Yelp but only one positive comment from his friend with make him go.
I also identified 3 types of potential users of my service, they are foodie, foodie's friend, and ethnic food expert. What interested me was, all international people I interviewed expressed that they are knowledgeable about food from their hometown but extremely ignorant about some other ethnic food, especially those they don't see much in their home country. I use the ethnic food expert persona to represent this user group. From a content generation and consumption perspective, these 3 groups of users are located at different spots.
Users can't get recommendations
when they need them urgently
For example, users can't get any recommendations when they want to find a good restaurant in 10 minutes, in the neighborhood they are at.
The map function allows users to find recommendations nearby them and at designated areas.
Restaurants I like VS I want to try
Users not only collect restaurants they like but also what they want to try in the future. They don't want to mix these two categories up.
2 tags: I like and I want to try
Users can add tag "I like" and "I want to try" to restaurants in their collection to distinguish these two types.
Most foodies have their lists
The “Foodies” prefer to document restaurants in lists. It makes organization, search and recommendation easier.
The service allows users to build and share their restaurant lists as well as look for other users' lists.
This project was my first exercise as I began learning UX and UI. Reviewing it after finishing it for a year, I found it has a bad looking and is pretty confusing to use. To revise this project, I began with a redesign of its info architecture.
Divergence on IA:
The refinement process involved multiple rounds of back and forth between the design of
the information architecture and paper prototype.
Revised Info Architecture:
the app is clearly separated into 3 sections: Friends, Me and Map
User Flow 1:
Explore friends' lists
User Flow 2:
Search restaurants nearby, check out details of a restaurant and go.
User Flow 3:
Add a restaurant to My Lists.