How travel preference matching works: turning a traveler's brief into the right trip

Travel preference matching takes what a traveler wants, such as budget, dates, pace and interests, and ranks trips and offers by how well they fit. Systems do it with hard rules and filters, by comparing offer features, by learning from what similar travelers chose, or with large language models that read a plain-language brief. Better briefs give better matches.

Here is how each approach works, what a good traveler brief contains, the transparency and privacy rules around matching, and where an advisor still beats the algorithm.

What is travel preference matching?

Travel preference matching turns what a traveler wants into a ranked list of trips to show first. Google's machine learning course describes the same job in general terms. A recommendation system uses a query, meaning the information a system uses to make recommendations, to pick from a set of items, the entities a system recommends.

In travel, the query is the traveler and their brief. The items are hotels, flights, tours, cruises and whole itineraries.

Most systems work in three stages. The first starts from a potentially huge corpus and generates a much smaller subset of candidates. The second scores and ranks those candidates. The last must take into account additional constraints for the final ranking.

Explicit preferences vs implicit signals

A matching system learns about a traveler in two ways.

Explicit preferences are what the traveler tells you. Google's course calls this explicit feedback, where users specify how much they liked a particular movie by providing a numerical rating. In travel, it is the brief: budget, dates, party size, pace and interests.

Implicit signals are what the system infers from behavior. In Google's example, if a user watches a movie, the system infers that the user is interested. In travel, that means past trips, the offers someone clicked or saved, and the ones they ignored. Google lists items that users previously interacted with as part of the query, alongside context such as time of day and the user's device.

What this means for agencies: explicit preferences are clear but only as good as the questions you ask. Implicit signals are plentiful for big booking sites and thin for a small agency with a few trips per client. For most advisors, the brief does the heavy lifting.

How do travel recommendation systems work?

There are four main approaches. Real systems often combine them.

Rules and filters

The simplest method. You set hard constraints and remove anything that breaks them: over budget, wrong dates, no direct flight, not wheelchair accessible. Google's course describes filters that remove some candidates as one way a system applies additional criteria or constraints.

Rules are predictable and easy to explain. They don't rank what is left. Ten hotels can pass every filter and still differ a lot in fit.

Content-based filtering

Content-based filtering uses item features to recommend other items similar to what the user likes. If a traveler loved a small boutique hotel in a walkable old town, the system looks for other offers with those features.

It needs no crowd. The model doesn't need any data about other users and can recommend niche items. The catch: it can only make recommendations based on existing interests of the user, and its features are hand-engineered to some extent. Someone has to tag every hotel well.

Collaborative filtering

Collaborative filtering uses similarities between users and items simultaneously to provide recommendations. It is the "travelers like you also booked" approach.

Its strength is surprise: it can help users discover new interests. Its weakness is new things. If an item was not seen during training, the system can't create an embedding for it, which Google says is often called the cold-start problem. A new client or a newly launched tour starts with nothing to learn from.

Matching with large language models

Large language models (LLMs) read text, so they can work from a brief written in plain words. A 2023 survey of LLMs in recommendation, a preprint that has not been peer-reviewed, sums up their edge. It credits them with high-quality representations of textual features and extensive coverage of external knowledge to establish correlations between items and users.

That matters for cold start. A 2025 survey, also a preprint, calls the cold-start problem one of the long-standing challenges in recommender systems. It reviews how LLMs, which possess strong capabilities in modeling user and item information, are being applied to it.

What this means for agencies: an LLM can turn "relaxed, foodie, no early starts, under $6,000 for two" into a shortlist without past booking data. Treat what it produces as a draft. Check every price, date and availability against the supplier before it reaches the traveler.

The four approaches at a glance

What should a good traveler brief contain?

A brief is the explicit half of matching. The table below is a practical checklist, not a legal standard.

Four rules make a brief useful:

1. Separate must-haves from nice-to-haves. Must-haves become filters. Nice-to-haves become ranking signals.
2. Ask what the budget includes. Total vs per person, and flights in or out, changes every result.
3. Ask about the last trip. "What would you change?" tells you more than "What do you like?"
4. Write it in the traveler's words. An LLM can use the phrasing, and you can quote it back in the proposal.

Do matching systems have to explain their recommendations?

Some do. In the EU, the Digital Services Act (DSA) covers this. It defines a recommender system as a fully or partially automated system used by an online platform to suggest in its online interface specific information to recipients of the service or prioritise that information. Article 27 requires online platforms that use one to explain how it works. They must set out the main parameters used in their recommender systems in plain language in their terms and conditions. Those parameters must include the criteria which are most significant and the reasons for the relative importance of those parameters.

Scope is limited. That section of the act shall not apply to providers of online platforms that qualify as micro or small enterprises (Article 19), unless they are designated very large online platforms. Most of the act has applied since February 17, 2024 (Article 93). This is a summary, not legal advice.

In the US, the Federal Trade Commission (FTC) has warned about design that steers people. Its 2022 dark patterns report names comparison shopping sites that claim to be neutral but really rank companies based on compensation. It also flags data-sharing choices designed to intentionally steer consumers toward the option that gives away the most personal information.

What this means for agencies: if commission or supplier deals affect what you show first, say so. A short "why we picked these" line in each proposal costs nothing and builds trust.

What traveler data should you collect for matching?

Only what changes the match. The FTC's guide for business puts it plainly: Keep only what you need for your business. It adds: don't collect and retain personal information unless it's integral to your product or service.

GDPR, which can apply when you serve people in the EU, sets the same bar. Data must be adequate, relevant and limited to what is necessary for the purpose (Article 5(1)(c)). Matching on preferences can also count as profiling, which GDPR defines to include using personal data to analyse or predict a person's personal preferences, interests, reliability, behaviour, location or movements.

Take extra care with dietary and accessibility needs. In our reading, they may reveal health information or religious beliefs. GDPR Article 9 restricts data concerning health and data revealing religious or philosophical beliefs unless an exception applies, such as explicit consent. Ask only when it affects the trip, and record why. For how to store it, see CRM for travel agents.

Where do human advisors beat matching algorithms?

Practical guidance, based on how the methods above work:

• Reading between the lines. A brief says "relaxing." The advisor hears that the last trip was exhausting and plans fewer transfers.
• Cold start. Every new client is a cold start for collaborative filtering. A 20-minute conversation fills the gap faster than months of clicks.
• Trade-offs. Algorithms rank. Advisors explain: "This hotel is $400 more, and here is what you get for it."
• Supplier knowledge. You know which property just renovated and which tour runs late. That rarely shows up in an item's features.
• Checking the output. An AI shortlist is a draft. The advisor confirms live prices and availability before sending.
• Following up. A match is the start of a sale. A good follow-up routine turns it into a booking.

The algorithm finds the options. The advisor makes the choice feel right. Use matching for speed and keep the judgment with a person. For more on AI drafting, see AI itinerary planning. To capture briefs from your website, see quiz funnels for travel agents.

How Tripdash matches travelers to offers

Marlow is Tripdash's agency-internal AI, built to find offers that fit your travelers. "Travelers are matched with offers that suit them." For advisors, Marlow qualifies travelers and routes briefs to advisors based on specialty and availability. See how Marlow works.

Sources

1. Google for Developers. Recommendation systems: Terminology, Machine Learning course, last updated August 25, 2025.
2. Google for Developers. Recommendation systems overview: Types, Machine Learning course, last updated August 25, 2025.
3. Google for Developers. Content-based filtering basics and Content-based filtering advantages and disadvantages, last updated August 25, 2025.
4. Google for Developers. Collaborative filtering basics and Collaborative filtering advantages and disadvantages, last updated August 25, 2025.
5. Google for Developers. Re-ranking, Machine Learning course, last updated August 25, 2025.
6. Wu, L. et al. A Survey on Large Language Models for Recommendation, arXiv:2305.19860 (preprint, not peer-reviewed), v5, June 18, 2024.
7. Zhang, W. et al. Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap, arXiv:2501.01945 (preprint, not peer-reviewed), v2, January 16, 2025.
8. European Union. Regulation (EU) 2022/2065 (Digital Services Act), October 19, 2022. Articles 3(s), 19, 27 and 93.
9. Federal Trade Commission. FTC Report Shows Rise in Sophisticated Dark Patterns Designed to Trick and Trap Consumers, September 15, 2022.
10. Federal Trade Commission. Protecting Personal Information: A Guide for Business, October 2016.
11. European Union. Regulation (EU) 2016/679 (General Data Protection Regulation), April 27, 2016. Articles 4(4), 5(1)(c) and 9.