Foodie Restorānu ieteikumu lietotne by Tianyi Qi

Foodie Restorānu ieteikumu lietotne by Tianyi Qi is a highly esteemed winner of the very prestigious 2022 Bronzas balva A' Design Award in Mobilo tehnoloģiju, lietotņu un programmatūras dizains Competition Category.

BronzasMobilais· 2022

Foodie Restorānu ieteikumu lietotne

by Tianyi Qi

Client: Tianyi Qi

Bronzas balva A' Design Award Winner in Mobilais Category

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Verified Award Winner

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The Design

About Foodie Restorānu ieteikumu lietotne

Foodie ir mobilā lietotne, kas izstrādāta, lai palīdzētu cilvēkiem atrast restorānus, izmantojot draugu ieteikumus. Ēdienu izvēles apspriešana parasti notiek klātienes sarunās, un Foodie pārnes šo apmaiņu uz digitālu platformu, kas pieejama jebkurā laikā un vietā. Ekrānā "Draugi" tiek rādīti atjauninājumi un draugu izveidotie saraksti. Ekrāns "Mani saraksti" ļauj lietotājiem pārvaldīt saglabātos restorānus un tos filtrēt pēc veida. Ekrāns "Karte" nodrošina iespēju meklēt restorānus tuvumā vai ap izvēlētu atrašanās vietu. Foodie ir izstrādāta iOS ierīcēm.

The Recognition

About the Bronzas balva A' Design Award

Foodie Restorānu ieteikumu lietotne ir Bronzas balvas A' Design Award ieguvējs, ļoti prestiža atzinība, ko piešķir izciliem dizainiem, kas izceļas ar radošumu, atjautību un viedumu. Bronzas ieguvēji ir pietiekami prasmīgi un pārsteidzoši radoši darbi, kas apliecina to dizaineru plašo pieredzi, iztēli un izdarību. Sagaidāms, ka Foodie Restorānu ieteikumu lietotne būs augsti vērtēts, apbrīnots un cienīts priekšmets, ievērojami profesionāls un inovatīvs. Šis ar Bronzas balvu apbalvotais dizains iekļauj labāko praksi mākslā, zinātnē, dizainā un tehnoloģijās un izceļas ar uzmanību detaļām. Foodie Restorānu ieteikumu lietotne lieliski apmierina vajadzības, skar vēlmes, valdzina jūtas un piesaista zinātkāri.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: Foodie is a mobile app that aims to leverage the action of sharing personal preference with one's social circle, which happens mostly in offline settings, to a digital platform. This transformation allows this information-sharing process to expand beyond limited scenarios so users can create content and access information whenever and wherever they need it.
Dimensions & Packaging: The app is designed for iOS systems.
Tags: Social, Restaurant, Food, Application, Recommendation
Production Technology: The app requires a strong search engine, map and navigation service, and restaurant database to be functional.
Design Challenge: Why do users use this service when there are already other popular information sources/services existing around? Which was the major challenge I was facing at the beginning stage of the project. The research was designed and planned to answer this question.
Research: 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:
1. How and when do people use each information source to find restaurants?
2. What do people trust about each information source?
3. How do people recommend restaurants to their social circle?

The results showed that social circle is the most trusted and the 2nd most used information source when participants are looking for restaurant recommendations. This insight validated the foundation of the project: even though modern technologies have played an important role in helping people find restaurants, people still get recommendations from their friends. From the research, I also learned the requirements participants have on a restaurant's recommendation service and the data gathered from the research was used in persona creation.
Inspiration: As a foodie living in New York City, I have been enjoying using apps like Yelp and Google to find restaurants, especially those that provide ethnic food that I have never tried before. However, I noticed that even though those apps are backed with big data, I trust my friends more in terms of finding restaurants. This experience makes me curious about this totally different mechanism of trust: How might we help people find restaurants from their friends' recommendations?
Project Duration: The project started in August 2019 and finished in January 2020, in New York City.
Operation Flow: On the "Friends" screen, a user will see new updates from their friends, for example, a friend added a new restaurant to their list. They can also explore all the lists created by their friends. To manage restaurants on their own lists, a user can easily navigate to the "My Lists" screen. They can use the filters to easily find the specific types of restaurants on their lists. On the "Map" screen, a user can search for restaurants nearby or around a certain location.
Image Credits: Image #1-5: Tianyi Qi