Omakase
an in-app software widget that optimizes research and deliberation for a fun and frictionless social dining experience
client/audience
Cornell Students
timeframe
Jan 2022 - May 2022
role
UX Designer
UX Researcher
This project targets groups of friends who want to quickly, smoothly, and fairly decide on (new) restaurants to try; however, existing services lack decision making capabilities for groups of users with individualized preferences.
Our solution streamlines and personalizes the research & deliberation phases to ensure a frictionless social dining experience for our users.
The Overview
Balancing diverse preferences fairly while deciding on a dining venue within friend groups presents two main challenges:
The Problem
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the tedious planning process (the gathering of preference, the research of restaurants, the discussion of options, and the final voting)
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the status quo where existing programs fail to effectively simplify this process
The Process
Trends from interviews reveal that preferences are implicitly known and not explicitly gathered. Moreover, it's notable that member influence is unbalanced within groups where quieter members feel unrepresented in the final choices. On the other hand, most or many options shared by the more active/assertive member are never opened.
Google Maps, Yelp, & TikTok are effective for finding & saving restaurants. However, as they primarily support searching by keywords & filtering by certain criteria, the initially dense information must be manually narrowed down by the user. A solution has yet to take a recommend-and-vote approach to finding new restaurants.
The Status Quo
The Problem Statement Revised
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gathering preferences needs to be deliberate and mutual
Groups of friends want to quickly & smoothly decide on where to eat, but a decision that satisfies every member’s preferences is difficult because:
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researching options is time-consuming
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deliberation can be unfair and cause friction
Quotes from
User Interviews
“The biggest challenge of making recommendations for my friends is that it’s hard to keep track of what everyone likes and dislikes to eat, especially when it changes almost every day.”
“Sometimes no one takes that initiative… because we just don’t want to bring up our individual preferences that other people in the group might dislike…”
“I love research, so Yelp is great for me, but it sucks that my friends sometimes don’t click the links that I send them, probably because it’s too much information.”
"Especially when there are less people in the group, everyone lowkey tries to be super accommodating because there are so few of us and we want each other to be satisfied with their time."
Our team collaborated to brainstorm and sketch out more than forty different ideas that address the pain points.
The Initial Prototypes
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users will be able to put in their dining restrictions and preferences to receive a list of restaurants that meets everyone’s needs
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users anonymously and asynchronously vote on a restaurant
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widget provides the best-match restaurant and allows users to access information about the restaurant and make a reservation if need
After studying the prototypes, we decided to combine several ideas and create an in-app widget that is seamlessly integrated into group chats. To streamline the decision making process:
User Scenario
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group decides to eat together
group members independently explore options based on their own preferences
tech acknowledges each member's preference and generates final recommendations that are subject to a voting process
final decision made through vote
and group proceeds to dine
A low-overhead recommendation and voting system that streamlines the process of gathering preferences, researching restaurants, and deliberating available options so that users can focus on their food and their friends.
The Gist: design idea one-liner
upon being activated, the widget bot (operated by our team member) privately messages all users in the friend group to collect each of their preferences
the widget bot is activated within the group chat by users
widget bot produces a list of recommendation based on the preferences of every user in the friend group acquired individually
users in the group chat respond to the widget bot by voting with a corresponding emoji representing the preferred restaurant
the widget bot calculates the votes and announces the result
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Widget of Oz
To improve our interface, refine our user flow, and better understand the algorithm, our team implemented the Wizard of Oz prototyping technique, an approach commonly used in early product development which involves simulating system functionality without building the actual technology. In our case, our team members took the role of the "wizards" who manually operated the recommendation and vote system for our test users and their friend group. This method allowed us to test our ideas and collect feedback for later iterations.
upon being activated, the widget bot (operated by our team member) privately messages all users in the friend group to collect each of their preferences
the widget bot is activated within the group chat by users
widget bot produces a list of recommendation based on the preferences of every user in the friend group acquired individually
users in the group chat respond to the widget bot by voting with a corresponding emoji representing the preferred restaurant
the widget bot calculates the votes and announces the result
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Feedback from the Wizard of Oz Study
User Response
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the current experience can seem not very comprehensive because it usually takes multiple rounds of voting to come to a decision
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asynchronous voting takes too long
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appreciate the visibility of who has already voted; however, some also prefer the comfort of voting anonymously
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linked information sometimes unused
Changes Made
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provide secondary options or allow refreshing choices
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set voting deadline or expiration
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make announcements in the chat on who voted while conducting votes individually and privately
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summarize information in voting display
The Final Idea:
A Japanese phrase meaning, a meal consisting of dishes selected by the chef.
Omakase
Let Us Decide.
An in-app software widget seamlessly integrated into group chats, allowing you to:
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input your personal preferences and dietary restrictions
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initiate dining sessions with your friends to:
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receive recommendations that meet everyone’s preferences
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read summarized information on each restaurant
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cast votes asynchronously, and optionally anonymously!
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The Interaction
Initiate Session
Set/Update Preferences
Review Individualized Recommendations
Review Group/Standardized Recommendations
Vote (before the expiration ☻)
View Announced Result and Dine Away!
Skim through Group Profile for a history of previously visited restaurants
✔︎ more options are available through refreshing
✔︎ options are categorized by cuisine type, distance, and price range
✔︎ more options are available through refreshing
✔︎ options are categorized by cuisine type, distance, and price range
within groupchats
on favourite type of cuisine, favourite restaurant,
dietary restrictions, and more
Initiate Session within groupchats
Set/Update Preferences on favourite type of cuisine, favourite restaurant, dietary restrictions, and more
Skim through Group Profile for a history of previously visited restaurants
multiple rounds
of
pick and choose!
Review Individualized Recommendations
Review Group/Standardized Recommendations
✔︎ more options are available through refreshing
✔︎ options are categorized by cuisine type, distance, and price range
✔︎ more options are available through refreshing
✔︎ options are categorized by cuisine type, distance, and price range
Vote (before the expiration ☻)
View Announced Result and Dine Away!
The Reflection
The design process is often misinterpreted as solely focused on creating brilliant high-fidelity prototypes. However, this project, like all UCD projects, served as a reminder that visual and high-fidelity designs hold little value unless backed by thorough and comprehensive user research. User research should be the cornerstone of UCD, providing valuable insights into the behaviours, preferences, and pain points of the target audience. Without a deep understanding of the users, designers run the risk of creating solutions that miss the mark or fail to resonate with their intended audience.
In essence, the true value of the design process lies in its ability to bridge the gap between user needs and innovative solutions. While high-fidelity designs can certainly enhance the user experience, they are only as effective as the research and insights that underpin them.
