Faceted Search
Delish and The Pioneer Woman are high-scale recipe destinations where the core user value proposition is helping users quickly find the right recipe for their moment of need—across thousands of pieces of evergreen and seasonal content. As content libraries grow, traditional search and feed-based discovery becomes insufficient on its own. Users increasingly expect precision filtering, transparent recipe attributes, and faster paths to decision-making with minimal cognitive load. This project introduces a scalable faceted search system designed to improve recipe discovery across multiple brands by leveraging a shared taxonomy and consistent attribute framework.
“An improved recipe search experience may result in users finding their perfect recipe faster, potentially meaning fewer page views per visit, decreased scroll depth, or seemingly negative impacts to other KPIs.”
— HypothesisThe existing search experience relies heavily on:
Linear result lists with limited filtering capability
High dependency on scrolling and browsing behavior
This creates friction in helping users quickly evaluate and compare recipes, especially within large result sets.
However, this work requires careful consideration of business tradeoffs:
A more efficient discovery experience could reduce total page views per session and scroll depth—potentially impacting engagement-based KPIs in the short term.
This makes it critical to balance user efficiency with business outcomes.
Goals
Design a faceted search experience that:
Enables users to refine recipe results using meaningful attributes
Standardizes how recipe metadata is exposed across search and browse surfaces
Establishes a scalable framework that can extend across multiple brands and content types
– Existing search results
Supporting user insights
Research and behavioral signals consistently showed that users value, fast identification of relevant recipes, clear, scannable attributes in search results, reduced effort in comparing options
This reinforced the need for, exposed search, enhanced recipe card metadata (ratings, time, saves), tabbed search experiences (recipes vs. broader content types), persistent attribute labels on content surfaces
Approach
1. Taxonomy-driven filtering model
I defined with each brand a subset of the broader recipe taxonomy that was most relevant to user decision-making, prioritizing attributes such as:
Time
Difficulty
Dietary needs
Occasion and meal type
This ensured the system remained useful without becoming overwhelming.
2. Scalable design system integration
Rather than treating facets as a standalone feature, I partnered with engineering to design them as part of a broader content intelligence layer, which also powers:
Recipe card labels
Attribute-based metadata surfacing
Cross-surface consistency in search and feed experiences
This created a unified logic system for how recipe attributes are surfaced across the product.
3. Multi-brand implementation strategy
A key aspect of this work was designing for reuse across Delish and The Pioneer Woman, each with distinct audiences and editorial tones.
The system was designed to:
Support shared underlying data structures
Allow brand-specific presentation layers
Scale to additional lifestyle brands in the future
Anticipated Outcomes
While not yet fully launched, early modeling and directional analysis suggest:
+5% increase in sessions per user (lifetime value uplift)
2% decrease in views per session, indicating faster path-to-decision and reduced friction in discovery
These metrics reflect a deliberate shift from volume-based engagement toward intent-driven efficiency and higher-quality user sessions.
Strategic impact:
This work establishes a reusable discovery framework that:
Improves how users evaluate large content sets
Reduces reliance on infinite scroll behavior
Creates a consistent attribute language across brands
It also lays the foundation for extending faceted discovery into other high-volume content verticals beyond recipes.