How do I ensure my traveler-interest recommendations with an attractions recommendation engine are compliant?

To make traveler-interest recommendations compliant, treat the engine as a regulated personalization system and build compliance into the data, model, and product layers—not just the UI.

1) Define what data you use

Only use data you can justify and document, such as:

• Explicit preferences: liked categories, saved places, trip purpose
• Session behavior: clicks, dwell time, searches
• Context: destination, season, budget range, party size
• Optional profile data: language, accessibility needs, travel dates

Avoid or heavily restrict:

• Sensitive personal data unless strictly necessary and explicitly consented
• Inferred sensitive traits
• Data from unclear third-party sources
• Cross-device or ad-tech data without proper notice/consent

2) Get clear consent and notice

Provide a plain-language notice that explains:

• What data you collect
• Why you use it
• Whether recommendations are personalized
• Whether data is shared with partners
• How users can opt out or delete data

If required by your jurisdiction, get opt-in consent for:

• Cookies or tracking
• Marketing personalization
• Sensitive data processing

3) Minimize and segment data

Use the minimum data needed to recommend attractions:

• Don’t store raw logs longer than necessary
• Separate identifiers from behavior data
• Use aggregation or pseudonymization where possible
• Limit access by role and purpose

4) Avoid discriminatory or harmful outcomes

Test whether recommendations systematically disadvantage groups or create unfair exposure. For example:

• Don’t suppress attractions based on age, gender, ethnicity, religion, disability, or other protected traits
• Be careful with proxies like neighborhood, device type, or language
• Check for bias in ranking, visibility, and click-through outcomes

If your engine influences pricing, eligibility, or access, compliance requirements become much stricter.

5) Make explanations and controls available

Give users:

• “Why am I seeing this?” explanations
• Ability to edit interests
• Ability to turn personalization off
• Ability to reset recommendations
• Access to their data and deletion options

6) Respect local privacy laws

Depending on where you operate, you may need to comply with:

• GDPR / UK GDPR
• CCPA/CPRA
• ePrivacy / cookie rules
• Consumer protection and anti-discrimination laws
• Platform-specific rules if you use ad platforms or app stores

Key GDPR-style obligations:

• Lawful basis for processing
• Data subject rights
• DPIA for high-risk profiling
• Vendor contracts
• Cross-border transfer safeguards
• Retention limits and security

7) Put governance around the model

Maintain:

• Data inventory and purpose documentation
• Model cards or system cards
• Audit logs for recommendation decisions
• Periodic bias and privacy reviews
• Incident response for data breaches or model issues
• Human review for high-impact cases

8) Validate third-party content and partners

If attractions come from partners or affiliates:

• Ensure contract terms allow the use
• Confirm data provenance
• Check whether they are joint controllers/processors where applicable
• Verify marketing and tracking compliance

9) Secure the system

Use:

• Encryption in transit and at rest
• Least-privilege access
• Secrets management
• Monitoring for abuse
• Secure deletion policies
• Regular penetration testing where appropriate

10) Build a compliance checklist

A practical checklist:

• Data map completed
• Privacy notice published
• Consent flows implemented where needed
• Opt-out and deletion available
• Sensitive data blocked or separately governed
• Bias testing performed
• Retention policy enforced
• Vendor agreements reviewed
• Security controls in place
• DPIA/PIA completed if required
• Audit and logging enabled

If you want, I can turn this into:

1. a GDPR/CCPA compliance checklist,
2. a product requirements document, or
3. a model governance policy for your attractions recommender.