How we designed a personalized experience to help users discover the perfect library space with easy access to resources, events, and community engagement.
Explore Case StudyLibrary users waste precious time juggling between multiple websites to find suitable study spaces that match their specific needs for quiet areas, reliable internet, and comfortable seating.
Library Finder (LIFI) is a personalized recommendation app for students and professionals that matches users with ideal library spaces based on their specific requirements.
With 200+ public libraries in NYC, our solution ensures people find and access these resources hassle-free.
As a UX designer on this project, I led the user research phase, including field visits, interviews, and survey design. I created user personas and journey maps to identify key pain points, then collaborated on wireframing, prototyping, and usability testing.
I was responsible for designing the personalization framework that matches user preferences to library attributes, ensuring an intuitive yet powerful filtering system.
To create an effective library finder application, we first needed to thoroughly understand our users' experiences with libraries, their pain points, and how they access available resources and services.
We visited 5 diverse libraries in NYC to observe user behavior and environmental factors:
Key Insight: Libraries offer vastly different environments, resources, and community spaces that aren't effectively communicated through their websites.
During our site visits, we conducted interviews with library users and staff members to gain insights into the environment and user patterns, as well as understand the challenges faced by both users and library staff.
Why This Matters: Speaking directly with users revealed that most prioritize environmental factors like noise level, seating comfort, and lighting over specific resource availability when choosing a library.
We conducted a brief survey to reach a wider audience and gather additional insights on:
Key Finding: Users visit multiple different websites when researching library options, causing significant friction in the decision-making process.
All libraries actively engage with their local communities, offering programs, events, exhibitions, and career advancement programs that reflect diverse interests and needs.
However, many users were unaware of events relevant to their interests due to discovery challenges.
Libraries feature diverse collections beyond traditional books, including multimedia resources such as DVDs, audio recordings, posters, and other archival materials that cater to various interests.
Libraries offer specialized resources that many users didn't know existed, leading to missed opportunities.
Libraries provide a variety of spaces to cater to different needs, including quiet study areas, collaborative workspaces, meeting rooms, and specialized rooms for specific activities.
Most users prioritize environmental factors like noise level, natural light, and seating comfort when choosing a library.
Users struggle to find comprehensive information about libraries, often visiting multiple websites to piece together details about hours, amenities, and resources.
On average, users spend significant time researching before selecting a library to visit.
Our interviews and surveys revealed consistent patterns in how users think about libraries:
I spend more time researching libraries than actually studying at them. By the time I find a quiet spot with good Wi-Fi, I've lost motivation to work.
Libraries should be meeting points for people and ideas, not just book repositories.
In many quiet study areas, sound insulation is often not very good.
Design Communications Grad Student
Susan likes going to libraries to study and work on her assignments. She feels it's very important for her to feel cozy and relaxed while studying in the library. She wants a library which is close to a subway station, making the commute shorter.
"My ideal work space involves a quiet, comfy chair, regulated air temperature, natural sunlight, a cup of tea, and not much movement in the surrounding area."
Susan represents our primary user segment: students and professionals who use libraries primarily as workspaces. Her needs drove our prioritization of features, focusing on workspace characteristics, amenities, and commute factors over traditional catalog-focused solutions.
Susan creates a study plan, outlines assignment requirements, identifies necessary resources, and estimates time needed.
Pain points: Uncertainty about finding specific resources
Researches library locations, opening hours, and commuting options using Google Maps, verifies available facilities.
Pain points: Time wasted on multiple websites, outdated information
Takes multiple subways, stops for coffee or food on the way, walks to the selected library.
Pain points: Long commute time, uncertainty about space availability
Searches for a well-lit, quiet area with outlets, connects to WiFi, and locates necessary equipment.
Pain points: Slow WiFi, limited outlet availability
Works on the assignment, explores library database for resources, asks librarians for help as needed.
Pain points: Time spent learning unfamiliar database systems
Completes assignment, returns borrowed materials, commutes back home.
Pain points: Exhaustion from the entire process
Mapping Susan's journey revealed that the planning and exploration phases (before even visiting a library) accounted for nearly 45 minutes of a typical 6-hour library visit—presenting a significant opportunity for improvement.
This insight led us to focus on creating a solution that would dramatically reduce pre-visit planning time through personalized recommendations.
Our design process followed a user-centered approach, focusing on solving the key pain points identified during research. We iteratively developed and tested our solution to ensure it effectively addressed user needs.
Based on our research, we defined the core problem: users waste precious time juggling between multiple websites and apps to find a suitable library space. They struggle to find locations that match their specific needs for quiet study areas, reliable internet, comfortable seating, and accessible resources.
How might we create a well-rounded recommendation system that helps users find libraries based on their personal preferences for workspace characteristics, resource availability, community events, and location convenience?
We developed a comprehensive taxonomy of library attributes across four key domains: physical space characteristics, media resources, community events, and services/equipment. This allowed us to create a structured framework for matching user preferences to relevant attributes.
Creating a comprehensive attribute system was crucial for enabling personalized matching. By categorizing library features into these four domains, we could create weighted recommendations that prioritized the factors most important to each user.
We created paper prototypes for three main areas: onboarding/preference capture, discovery feed, and detailed library profiles. Testing these with users helped us refine our concepts before moving to digital design.
Key Learning: Users preferred visual preference selection over text-based filters, which informed our digital wireframing approach.
Based on insights from paper prototype testing, we created medium-fidelity wireframes in Figma, focusing on the core user flows: preference setting, browsing recommendations, searching for specific resources, and exploring library details.
The most significant design challenge was creating a system that could handle complex filtering preferences while maintaining a simple, intuitive interface. Early user testing showed that traditional filter interfaces overwhelmed users with too many options.
To solve this, we developed a progressive preference capture system that uses visual cards for initial preferences, then reveals more detailed options contextually based on those initial choices.
We conducted usability tests with participants representing our target audience. Tests focused on evaluating the effectiveness of our preference capture system, the relevance of recommendations, and the overall ease of navigation.
Key Improvements: Based on testing feedback, we simplified the onboarding flow, reorganized library profiles for better information hierarchy, and enhanced the map interface with clearer visual cues.
Our solution is a comprehensive recommendation app that transforms how users discover and select library spaces. LIFI integrates four key components to provide a seamless library discovery experience:
The app uses a sophisticated matching algorithm that connects users with libraries based on their specific preferences across multiple dimensions, eliminating the need to search through multiple websites.
Rather than overwhelming users with complex filter interfaces, we created a simple onboarding flow that captures key preferences and translates them into weighted attributes for matching.
LIFI goes beyond just finding physical spaces by integrating comprehensive resource discovery across all libraries in the network, eliminating the need to search individual library catalogs.
Many users were unaware of specialized resources. The unified discovery system increases visibility and utilization of valuable but often overlooked library offerings.
Our research showed that commute time and location convenience were major factors in library selection. We developed a map-based interface that incorporates transit data and nearby amenities to help users make more informed decisions.
Libraries host numerous events that often go undiscovered. LIFI incorporates personalized event recommendations based on user interests, increasing awareness and participation in library-hosted community activities.
This is exactly what I've been looking for. I would definitely use this app regularly—it would save me so much time and frustration.
This project reinforced several important UX principles:
The biggest challenge was balancing comprehensive filtering with usability. While we successfully addressed this for most scenarios, there remains an opportunity to further refine the advanced filtering options for power users.
With additional time and resources, I would explore integrating real-time occupancy data to help users find available seating during peak hours—a frequently requested feature during user testing.
