Uber Recs
The Burger Workshop
explore data & AI ethics thru eviction data
The Burger Workshop
explore data & AI ethics thru eviction data
What if drivers recommend
their hometowns to riders
through the Uber app?
The Burger Workshop
explore data & AI ethics thru eviction data
The Burger Workshop
explore data & AI ethics thru eviction data
Digitalize Word-of-Mouth,
One Ride at a Time.
The Burger Workshop
explore data & AI ethics thru eviction data
The Burger Workshop
explore data & AI ethics thru eviction data

Problem Space

Tourists are struggling

We reviewed 10 academic papers on tourist decision-making models, and conducted interviews with 10 travelers, uncovering 3 key problems they commonly face.

Information Overload

Trust and Credibility

Uncertain Conditions

Uber Drivers and Tourist Riders

When riders travel to a new city, they often crave authentic local experiences, but they lack personal connections. On the flip side, drivers know and love their city. They want to share it, but don’t have a clear or comfortable way to do so. Both sides want to connect, but right now, there’s no smooth or reliable way to make that happen.

Tourist Riders

Have no personal connections with locals of the city they are visiting

Know and love their city, but have no clear way to share it

Unsure whether locals are willing or available to offer recommendations

Worry that their recommendations may be unsolicited

Interacting with locals can be unpredictable, awkward, and rarely documented

Struggle to fully convey their recommendations while driving

Uber Drivers

How might we

How might we

How might we

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

Research Question

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Question

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Question

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Question

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Market Analysis

Research Finding

Localness

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Finding

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Survey

215

Participants

20+

Platforms

Distributed

21

Total

Questions

100%

Completion

Rate

Research Finding

Localness

95% want recs from locals; 82% of those request recs from Uber drivers

Experiencing the locals’ lifestyle/customs provides an immersive travel experience that improves users fulfilment while traveling

Design Implication

Design methods that encourage local drivers to share travel insights, and enable users to easily discover hidden gems

Research Finding

Curate

Users prefer a few very personalized recommendations tailored to their trip purpose

Users are overwhelmed with too many platforms and options to choose from when planning. Showing users too many options is not helping

Design Implication

Help users focus on fewer, high-quality options, and provide sources of recommendation

Task Observation

Who: 5 participants of different travel styles, selected based on the survey. Why: Focused, observable behaviors for between-group comparison. What: Participants were given a travel-planning task with unexpected situations. We observed their tools and decision-making processes

Research Finding

Consensus: users rely on a consensus of reviews, ratings, and comments as social proof, yet they are susceptible to detailed negative feedback contradicting the consensus

Users are concerned about the authenticity of a positive consensus, but a large number of reviews helps alleviate this concern. Detailed negative feedback feels more personal than the generalized tone of positive reviews, and may create doubt about the consensus.

Design Implication

Similar recommendations should be grouped to provide social proof


Uber drivers and their recommendations should be verified to ensure they are trustworthy and authentically local



Diary Study

Who: 6 participants who were on vacation at the time. Why: real unexpected travel situations over a longer period. What: Participants were given a daily diary template, and self-report their decision-making processes during unexpected situations

Research Finding

Spontaneous: users often enjoy spontaneous exploration, discovering places of interest along their planned travel routes.

Travelers often deviate from their scheduled itinerary and end up filling in the gaps with activities nearby. They are open to unanticipated adventures and detours, which add a sense of impulsivity that they enjoy.

Design Implication

Design systems that suggest real-time, context-aware recommendations along the user’s route to help fill unexpected schedule gaps

Market Analysis

Research Finding

Localness

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Finding

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Survey

215

Participants

20+

Platforms

Distributed

21

Total

Questions

100%

Completion

Rate

Research Finding

Localness

95% want recs from locals; 82% of those request recs from Uber drivers

Experiencing the locals’ lifestyle/customs provides an immersive travel experience that improves users fulfilment while traveling

Design Implication

Design methods that encourage local drivers to share travel insights, and enable users to easily discover hidden gems

Research Finding

Curate

Users prefer a few very personalized recommendations tailored to their trip purpose

Users are overwhelmed with too many platforms and options to choose from when planning. Showing users too many options is not helping

Design Implication

Help users focus on fewer, high-quality options, and provide sources of recommendation

Task Observation

Who: 5 participants of different travel styles, selected based on the survey. Why: Focused, observable behaviors for between-group comparison. What: Participants were given a travel-planning task with unexpected situations. We observed their tools and decision-making processes

Research Finding

Consensus: users rely on a consensus of reviews, ratings, and comments as social proof, yet they are susceptible to detailed negative feedback contradicting the consensus

Users are concerned about the authenticity of a positive consensus, but a large number of reviews helps alleviate this concern. Detailed negative feedback feels more personal than the generalized tone of positive reviews, and may create doubt about the consensus.

Design Implication

Similar recommendations should be grouped to provide social proof


Uber drivers and their recommendations should be verified to ensure they are trustworthy and authentically local



Diary Study

Who: 6 participants who were on vacation at the time. Why: real unexpected travel situations over a longer period. What: Participants were given a daily diary template, and self-report their decision-making processes during unexpected situations

Research Finding

Spontaneous: users often enjoy spontaneous exploration, discovering places of interest along their planned travel routes.

Travelers often deviate from their scheduled itinerary and end up filling in the gaps with activities nearby. They are open to unanticipated adventures and detours, which add a sense of impulsivity that they enjoy.

Design Implication

Design systems that suggest real-time, context-aware recommendations along the user’s route to help fill unexpected schedule gaps

Market Analysis

Research Finding

Localness

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Research Finding

Aside from Airbnb, no existing platforms truly rely on locals for insights. Uber is uniquely positioned to do so through its network of local drivers.

Survey

215

Participants

20+

Platforms

Distributed

21

Total

Questions

100%

Completion

Rate

Research Finding

Localness

95% want recs from locals; 82% of those request recs from Uber drivers

Experiencing the locals’ lifestyle/customs provides an immersive travel experience that improves users fulfilment while traveling

Design Implication

Design methods that encourage local drivers to share travel insights, and enable users to easily discover hidden gems

Research Finding

Curate

Users prefer a few very personalized recommendations tailored to their trip purpose

Users are overwhelmed with too many platforms and options to choose from when planning. Showing users too many options is not helping

Design Implication

Help users focus on fewer, high-quality options, and provide sources of recommendation

Task Observation

Who: 5 participants of different travel styles, selected based on the survey. Why: Focused, observable behaviors for between-group comparison. What: Participants were given a travel-planning task with unexpected situations. We observed their tools and decision-making processes

Research Finding

Consensus: users rely on a consensus of reviews, ratings, and comments as social proof, yet they are susceptible to detailed negative feedback contradicting the consensus

Users are concerned about the authenticity of a positive consensus, but a large number of reviews helps alleviate this concern. Detailed negative feedback feels more personal than the generalized tone of positive reviews, and may create doubt about the consensus.

Design Implication

Similar recommendations should be grouped to provide social proof


Uber drivers and their recommendations should be verified to ensure they are trustworthy and authentically local



Diary Study

Who: 6 participants who were on vacation at the time. Why: real unexpected travel situations over a longer period. What: Participants were given a daily diary template, and self-report their decision-making processes during unexpected situations

Research Finding

Spontaneous: users often enjoy spontaneous exploration, discovering places of interest along their planned travel routes.

Travelers often deviate from their scheduled itinerary and end up filling in the gaps with activities nearby. They are open to unanticipated adventures and detours, which add a sense of impulsivity that they enjoy.

Design Implication

Design systems that suggest real-time, context-aware recommendations along the user’s route to help fill unexpected schedule gaps

Brainstorm and Ideation

Brainstorm and Ideation

Brainstorm and Ideation

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

Combined strategies are needed here

Three Themes

The three tourist challenges were translated into three themes for ideation. Each team member generated 10 design concepts inspired by research findings, and all ideas were categorized under the three core themes as shown in the Venn diagram. While many ideas successfully covered two themes, none fully encompassed all three. We realized that addressing them all would require a combination of strategies. Following the brainstorming session, each team member selected five design ideas with the most potential and developed sketches to better understand how these ideas could fit within the context of the Uber app.

Information Overload

Personalized

Trust and Credibility

Trust

Uncertain Conditions

Quick

Concept Testing - Round 1

We selected three concepts that were well-received by participants during concept testing round one. The first concept recommends points of interest (POIs) along the rider’s current route. The second reimagines POI selection through a dating app–style swiping interface. The third replaces traditional ratings and reviews with insights drawn from Uber’s unique trip data. While none of these concepts alone became the final product, each contributed critical elements to the overall solution.

Concept Testing - Round 2

Rider experience product flow: after gathering feedback from the first round of testing, we developed new sketches that began shaping end-to-end product flows. One flow focused specifically on the in-ride experience, mapping the full journey of a first-time user interacting with what would later become known as Uber Recs. This work marked the initial formation of the Uber Recs experience.

Driver experience product flow: we also developed a flow for the driver side, specifically within the driver app, exploring how local insights and recommendations could be prompted from drivers without adding significant burden. However, due to time constraints, the project scope was limited to focusing solely on the rider-side experience.

Final Concept

We translated the envisioned experience into concrete steps, procedures, and interface elements needed to support it. The flow diagram outlines the full user journey. This exercise helped identify the critical decision points, interface needs, and system triggers required to make the experience feel seamless and intuitive. With a clear understanding of both user flow and system behavior, we were ready to proceed to developing the high-fidelity prototype.

High Fidelity Prototypes

High Fidelity Prototypes

High Fidelity Prototypes

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

Current Ride-booking Page

Design Goal

Add Uber Recs into the flow.

Keep it simple and intuitive.

Iteration 1.0

Iteration 1.1

Solution 1

Each ride option (UberX, Black, etc.) includes a dropdown menu that lets users choose from nearby drivers.

Iteration 2.0

Solution 2

Display available drivers directly under each ride option, without adding a Recs mode toggle.

Iteration 3.0

Solution 3

All existing ride options remain unchanged, and users can simply choose drivers from a separate section called Uber Recs.

Open Uber at airport rideshare pickup

Book a ride to hotel with Uber Recs

Wait for pickup and view driver profile

Save driver recommendations

Approaching en-route recommendations

Book a ride with saved recommendations

Localness

Unlike generic tour apps, Uber Recs curates authentic insights sourced directly from local drivers.

Curation

When riders first arrive in a new city, their initial interaction with Uber Recs is designed to feel welcoming and inspiring. We introduce a specialized homepage that captures the local atmosphere, helping set the tone for exploration and putting riders into a "trip mode" mindset.

Spontaneity

In Recs Mode, riders book rides as usual, with an added option to choose a driver who shares their interests, like food or art, for spontaneous, personalized recs.

Curation

While waiting for the car to arrive, users receive a personalized greeting card introducing the driver. They also have the option to view the driver's detailed profile. This touchpoint is designed to help break the ice and facilitate more natural conversation once the ride begins.

Localness

In Recs Mode, the driver profile highlights the driver's local roots and lived experiences.

Curation

In addition to the current driver’s recs, users can also view suggestions from other drivers who share their travel preferences.

Consensus

Alongside the driver’s personal take on recommended POIs, the system shows how many other drivers have recommended the place and how many users have saved it. Similar reasons are grouped to reflect a broader consensus.

Spontaneity

If driver recs are on or near the route, they appear on the map. As the ride progresses, riders get notified just before passing a suggested spot, encouraging spontaneous discovery. The tipping page reminds users of the driver's recs, encouraging tips and offering another opportunity to save any they missed during the ride.

Notification on lock screen

Spontaneity

The following day, when considering where to explore, riders can easily recall saved recs suggested by their driver. From the homepage, riders can quickly book a ride to the saved destination, reducing the need for extensive research.

Localness

Users can also save drivers to explore their full list of recs. Discovering destinations through a trusted local adds a sense of authenticity and connection that goes beyond browsing generic reviews.

Localness

Unlike generic tour apps, Uber Recs curates authentic insights sourced directly from local drivers.

Curation

When riders first arrive in a new city, their initial interaction with Uber Recs is designed to feel welcoming and inspiring. We introduce a specialized homepage that captures the local atmosphere, helping set the tone for exploration and putting riders into a "trip mode" mindset.

Spontaneity

In Recs Mode, riders book rides as usual, with an added option to choose a driver who shares their interests, like food or art, for spontaneous, personalized recs.

Curation

While waiting for the car to arrive, users receive a personalized greeting card introducing the driver. They also have the option to view the driver's detailed profile. This touchpoint is designed to help break the ice and facilitate more natural conversation once the ride begins.

Localness

In Recs Mode, the driver profile highlights the driver's local roots and lived experiences.

Curation

In addition to the current driver’s recs, users can also view suggestions from other drivers who share their travel preferences.

Consensus

Alongside the driver’s personal take on recommended POIs, the system shows how many other drivers have recommended the place and how many users have saved it. Similar reasons are grouped to reflect a broader consensus.

Spontaneity

If driver recs are on or near the route, they appear on the map. As the ride progresses, riders get notified just before passing a suggested spot, encouraging spontaneous discovery. The tipping page reminds users of the driver's recs, encouraging tips and offering another opportunity to save any they missed during the ride.

Notification on lock screen

Spontaneity

The following day, when considering where to explore, riders can easily recall saved recs suggested by their driver. From the homepage, riders can quickly book a ride to the saved destination, reducing the need for extensive research.

Localness

Users can also save drivers to explore their full list of recs. Discovering destinations through a trusted local adds a sense of authenticity and connection that goes beyond browsing generic reviews.

Localness

Unlike generic tour apps, Uber Recs curates authentic insights sourced directly from local drivers.

Curation

When riders first arrive in a new city, their initial interaction with Uber Recs is designed to feel welcoming and inspiring. We introduce a specialized homepage that captures the local atmosphere, helping set the tone for exploration and putting riders into a "trip mode" mindset.

Spontaneity

In Recs Mode, riders book rides as usual, with an added option to choose a driver who shares their interests, like food or art, for spontaneous, personalized recs.

Curation

While waiting for the car to arrive, users receive a personalized greeting card introducing the driver. They also have the option to view the driver's detailed profile. This touchpoint is designed to help break the ice and facilitate more natural conversation once the ride begins.

Localness

In Recs Mode, the driver profile highlights the driver's local roots and lived experiences.

Curation

In addition to the current driver’s recs, users can also view suggestions from other drivers who share their travel preferences.

Consensus

Alongside the driver’s personal take on recommended POIs, the system shows how many other drivers have recommended the place and how many users have saved it. Similar reasons are grouped to reflect a broader consensus.

Spontaneity

If driver recs are on or near the route, they appear on the map. As the ride progresses, riders get notified just before passing a suggested spot, encouraging spontaneous discovery. The tipping page reminds users of the driver's recs, encouraging tips and offering another opportunity to save any they missed during the ride.

Notification on lock screen

Spontaneity

The following day, when considering where to explore, riders can easily recall saved recs suggested by their driver. From the homepage, riders can quickly book a ride to the saved destination, reducing the need for extensive research.

Localness

Users can also save drivers to explore their full list of recs. Discovering destinations through a trusted local adds a sense of authenticity and connection that goes beyond browsing generic reviews.

Usability Testing

Usability Testing

Usability Testing

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

design for tourist riders to receive and retain local insights from Uber drivers, supporting in-car interactions and encouraging future Uber usage through saved, actionable recommendations?

Overall Process Summary

👥

Recruited 12 participants.
9 frequent Uber riders, 3 former Uber drivers.

📱

Prototype was presented on a smartphone at high fidelity; users were encouraged to think aloud.

🤔

Users were not introduced to Uber Recs up front which allowed for observation of organic discovery.

💬

Each session ended with a semi-structured interview covering usability and expectations.

Task 1: Discover Uber Recs

Scenario: Users arrive at Atlanta airport and are booking a ride to their hotel.

Objective: Observe if users organically notice and interact with Uber Recs without prior introduction.

Task 2: Converse with the Driver

Scenario: A team member role-plays as the Uber driver during ride.

Objective: Observe if and how Uber Recs enables natural rider-driver conversations.

Task 3: Act on Saved Recs

Scenario: Users decide where to visit the next day in their hotel rooms.

Objective: Observe if users can find saved content and book a ride to it.

Localness

Test Finding

Users were drawn to recommendations from locals.

10/12 users (83%) trusted recommendations more when accompanied by a personal quote or a photo of the driver. Many described this as “real” or “memorable.”

Experts warned that personal narratives might risk discrimination during driver selection.

Experts highlighted the need for ethical safeguards if rider preferences or driver attributes are surfaced too explicitly.

Curation

Test Finding

Fewer, clearer recommendations helped, but not all users agree on how few is few enough.

11/12 users (92%) described the 3-recommendation format as “digestible,” “low-pressure,” and “easy to remember.” One user called the list “weirdly limited.” “There’s no way those are the only cool places nearby.”

Some users found driver-centered recommendations too heavy.

They preferred general suggestions that didn’t require interpreting the driver’s background or taste.

Consensus

Test Finding

Users seek social consensus, but only when it aligns with their own gut feeling.

7/12 users (58%) said they trusted recommendations more when grouped under “# of Drivers Picked This.” However, 9/12 (75%) disregarded the most popular recommendation and questioned the consensus.

Users instinctively look for negative reviews to "balance the story."

4/12 users (33%) demanded negative reviews, even though they found the layered consensus helpful.

Spontaneity

Test Finding

Users welcomed recommendations that popped up mid-ride, until it felt like too much.

7/12 participants (58%) saved or tapped on a recommendation when it appeared just before passing it.

One user said the pop-ups felt “like a nudge I didn’t ask for.”

Another asked for more control: “I want to decide when I’m in ‘spontaneous mode’, not have it assume I always am.”

© 2014-2023 Yijiang Xu

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