<ul><li>Online Travel Agencies (OTAs) utilize data to recommend hotels by collecting both explicit and implicit data from users.</li><li>Explicit data includes information like destination, travel dates, number of guests, budget, star rating, and amenities provided by users.</li><li>Implicit data collected by OTAs includes search history, browsing behavior, booking history, user reviews, device and location information, and sometimes social media connections.</li><li>Data collected, both explicit and implicit, is fed into algorithms to provide personalized hotel recommendations based on user preferences.</li><li>The system continuously learns and evolves, refining recommendations based on user interactions and feedback.</li><li>Benefits for travelers include a personalized experience, time savings, discovering hidden gems, and improved value when using OTAs for hotel bookings.</li><li>Understanding how these data-driven systems work can lead to better hotel choices and more satisfying trips for users.</li></ul>

How OTAs Use Data to Recommend Hotels: Decoding the Travel Algorithm

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