Understanding Profile vs. Preferences Fields for Matching & Recommendations

Overview

One of the most important concepts when setting up matching and recommendations in ScaleGrowth is the difference between a member’s Profile and their Preferences.

Using the right fields in the right place is critical for generating accurate matches – whether you’re connecting members to other members, to content, or to communities.

This guide walks you through:

• The difference between Profile and Preferences
• How matching works based on these fields
• Why using shared smart fields across Profile and Preference views enables high-intent recommendations
• How to model your fields to support strong, scalable matching logic

Understanding how to use Profile vs. Preference fields correctly is key to optimizing your platform’s discovery experience and unlocking the full power of ScaleGrowth’s Personalization engine.

🧠 The Core Analogy: Who You Are vs. What You’re Looking For

Think of the platform like a matchmaking system:

• Profile = who a member is, what they’ve done, what they offer
• Preferences = what a member is currently seeking or needs

Even when using the same field name or list of options, the context of where the field is placed changes the meaning completely.

Example:

• “Topics I can speak on” → goes in Profile
• “Topics I want to learn about” → goes in Preferences

This context-aware modeling is the foundation of ScaleGrowth’s intent-based matching.

🔁 How Matching Works

ScaleGrowth’s Matching & Recommendation Engine compares field values across members and content using multiple tiers of logic. Here's how the fields are used in matching:

Cross-Matching (Tier 1: High Intent, ✅ High matching weight)

• Your Profile matches another member’s Preferences
• Your Preferences match another member’s Profile
• Your Preferences match a piece of content’s profile fields

This is the strongest match logic, as it aligns intent with identity – similar to a skills exchange or a mentor-mentee model.

Same-Side Matching (Tier 2: Similarity, ⚠️ Lower weight )

• Your Profile matches another Profile
• Your Preferences match another Preferences
• Your Profile matches a piece of content

Still valuable, but these matches reflect similarity, not directional intent.

Implicit Matching (Tier 3: Fuzzy)

• Based on language patterns, shared keywords, behaviors, and collaborative filtering
• Not based on direct field-to-field matches
• Useful when structured data is sparse

For a full breakdown, see: How ScaleGrowth Designed Its Recommendation Engine for Member Platforms

🔁 TL;DR: Cross-matching carries more weight.

The strongest matches are made when:

• Your Profile matches Their Preferences
• Your Preferences match Their Profile

This gives the matching system context for both identity and intent, leading to smarter, more personalized results.

✅ Using Profile vs. Preferences the Right Way

You can create a single smart field or tag set (e.g. Tag Set 1) and apply it to both the Profile and Preferences views.

This enables:

• Profile: what the member can offer
• Preference: what the member is looking for

When used this way, it enables Tier 1 cross-matching, which is the highest-value recommendation path.

⚠️ Common Mistake: Using Two Smart Fields for the Same Concept

Some teams create two different smart fields – such as Tag Set 1 and Tag Set 2 – both placed in the Profile view, to represent “what I can offer” and “what I need.” Even if the options are identical, this setup leads to fuzzy, Tier 3 matching because the system treats them as independent, unrelated fields.

✅ Correct Modeling:

• Tag Set 1 in Profile = what I can offer
• Preference: Tag Set 1 in Preferences = what I need

This creates a direct, intent-based match between what one member needs and what another member offers.

🧠 When to Use Two Separate Fields

There are valid cases to use two smart fields if the option sets are different – for example:

• One field describes skills or experience
• The other describes interests or goals

In this case, they are not two sides of the same coin, and separate fields are appropriate.

⚙️ How to Configure Preferences

By default, all users have a Profile. The Preferences view is optional, but can be enabled to power more nuanced matching.

To set up a Preferences view on your platform:

1. Go to Admin
2. Navigate to Settings → Configuration
3. Open the Member Profile section
4. Under the Preferences View dropdown, select an option to enable it

Once enabled, you can define custom fields specifically for Preferences (like Tag Set 1) to the Preferences, just like you can for Profiles.

🧾 Adding Fields to Profile vs. Preferences

Refer to this guide to add Custom Field to Profile, and the same goes to Preference: Add/Edit Custom Profile Fields

You can create the same field name (e.g. Industry, Location, Skills) in both the Profile and Preferences views. When both exist:

• Profile fields describe the user
• Preferences fields describe what the user is seeking

When used in matching, the Preferences field takes precedence over the Profile field in determining relevance.

💡 Example:

Profile

• What industry are you in?
• Describes the member

Preferences:

• What industries are you targeting?
• Describes desired match

This lets the matching engine align supply (Profile) with demand (Preferences).

🧾 Collecting Data During Onboarding

During member onboarding or intake, you can collect:

• Profile fields (e.g., lived experience, skills, background)
• Preference fields (e.g., goals, support needs, interest areas)
• Or both

This gives you more flexibility in building rich, personalized matches.

👍 Best Practices

• Always define Profile fields for baseline identity data
• Enable and define Preferences when matching is a key platform feature
• Use the same field labels across both views if you want to match similar criteria
• Default to Preferences for matching logic when both field types are present
• Keep field types and options consistent to maximize match success

💬 Still Have Questions?

If you're unsure how to model your fields for matching, or need help cleaning up an existing configuration, reach out via the Success Center. Our team is happy to help.

📚 Related Knowledge Base Guides:

How ScaleGrowth Designed Its Recommendation Engine for Member Platforms

Add/Edit Custom Profile Fields

Add/Edit Custom Profile Fields for Onboarding

Managing Content Spaces for Home Feed and Recommendations

Using Platform-wide Tags

Best practices for using Tag Sets