AI and ML

Semantic Search For Travel Place Document

This project focuses on building a semantic search engine that understands the meaning behind user queries, not just keywords.

Developed by TechTIQ Inc., the solution uses Artificial Intelligence and Natural Language Processing (NLP) to deliver more accurate and relevant search results for travel-related content.

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Introduction

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Most traditional search engines rely on keyword matching. This approach often returns irrelevant results because it does not understand the context or intent behind a query.

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Semantic search improves this by analyzing the meaning of the input, allowing the system to return results that better match what the user is actually looking for.

Our Approach

The goal of this solution is to match user queries with the most relevant travel content based on meaning, not just exact words.

To achieve this, TechTIQ Inc. applied deep learning models and NLP techniques to process both user input and document data.

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Data
  • Collected over 31,000 travel articles from Wikitravel
  • Cleaned and removed duplicate or invalid content
  • Structured the data for efficient search processing
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Text Similar Model
  • Converted text (queries and documents) into embedding vectors using deep learning models
  • Compared similarity between vectors to identify the most relevant results
  • Returned top matching articles based on semantic relevance (default top 10 results)
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Key Benefits

More accurate and meaningful search results

Better understanding of user intent

Reduced irrelevant results compared to keyword search

Improved user experience for content discovery

Scalable for large datasets

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