How It Works:
Semantic understanding of queries: When a user types a query like “beach resorts for a family vacation,” the AI system does not just look for listings with the word “family-friendly” or “beach.” Instead, it understands the context of the query, drawing on vectors that represent family-friendly features, beach proximity, and other related criteria.
Contextual results: The system queries the vector database myanmar rcs data to return results that match the user’s preferences at a deeper level, improving the quality of the search results by focusing on intent and context rather than just keywords.
This approach creates a richer, more intuitive search experience for the user.
In the travel industry, visual content, such as photos and videos, plays a critical role in driving customer engagement. Vector databases allow travel platforms to vectorize images and generate recommendations based on visual similarity. By leveraging AI-generated content and semantic analysis, the platform can automatically suggest similar hotels, destinations, or experiences based on visual attributes.
How It Works:
Image-to-vector conversion: Travel photos (e.g., of a beach resort, a city skyline, or a scenic mountain view) are converted into vectors using image recognition and embedding models.
Visual search for similar content: When a user uploads a photo of a destination they are considering, the platform can query the vector database to find visually similar locations, hotels, or activities.