What should I read next?
A project to improve how people navigate and discover within the Barcelona library system
The Problem
The Hypotheses
The Research
Designing
Testing and Iteration
Reflection
The Problem
The Aladi catalog is the central system used to access books, media, and resources across Barcelona’s library network. Barcelona’s public library system offers a rich catalog, but the discovery experience is largely built around search. As a result, finding something new becomes effortful.
The Hypotheses
If people get a little guidance when they’re not sure what they want, they’ll be more likely to keep exploring instead of giving up.
When people are guided on what to read next, they are more likely to continue engaging with content.
If reading feels easy to return to, people will come back more often and build a habit.
The Research
User behavior
Most people come to the library hoping to find something new, not with a specific title in mind. Instead, on the website they’re met with a search-heavy experience that assumes they already know what they’re looking for. Without clear guidance, it’s hard to know where to begin.
As a result, people either fall back on familiar books or abandon the search altogether. Discovery often happens outside the platform, through friends, social media, or external recommendations, rather than within the library itself.
The “Recently returned” section surfaces reading options, but without images or context, they lack the visual pull to invite exploration.
System gaps
The current experience is optimized for retrieval, not exploration. While the catalog is extensive, it lacks mechanisms to surface relevant or personalized content.
There is no clear answer to “what should I read next?” within the product.
Key Insights
People don’t always know what they want, they need guidance
Too many options without structure leads to inaction
Supporting small decisions can build long-term habits
Designing
The focus shifted from a purely search-based system to one that supports discovery.
I introduced lightweight, contextual recommendations that help users navigate the catalog without requiring high effort or prior knowledge.
The goal was not to replace search, but to complement it, making the experience feel more guided and intuitive with the ability switch between recommendations and search.
Key features
Personalized recommendations based on past activity and interests
“What to read next” prompts to support low-effort decision-making
Contextual suggestions within the browsing experience
Flexible exploration paths based on genre or most read
Testing and iteration
Early concepts were tested around clarity and usefulness of recommendations. Initial feedback showed that overly generic suggestions were easy to ignore, while more contextual and behavior-based recommendations felt more relevant and actionable.
Iterations focused on:
Improving the relevance of suggestions
Reducing cognitive load in decision-making
Making recommendations feel integrated, not intrusive
Version 2 introduced recommendations, but they still lacked enough context to feel useful
In Version 3, adding images made recommendations more noticeable and engaging
Reflection
This project shifted how I think about discovery.
What initially seemed like a problem of access revealed itself as a problem of direction. Most people don’t come in knowing exactly what they want. They come in hoping to find something, but the system assumes they already have an answer.
Designing for discovery meant focusing less on search and more on guidance. Small decisions, like introducing context, adding imagery, or suggesting a clear next step, had a greater impact than expanding the catalog itself.
If I continued this project, I would explore how to make recommendations feel more personal over time, without requiring too much input from the user.
Purpose-driven at heart, I build products part of something greater ✩₊˚.⋆☾⋆⁺₊✧
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