Creating a Semantic Search Algorithm to Understand User Intent and Context, Improving Search Precision and Relevance

Challenge Background

In today's fast-paced digital era, the volume of information available is vast and continuously growing. Traditional keyword-based search engines often struggle to deliver precise and relevant results, leading to frustrated users and reduced efficiency. As our organization expands its digital presence and offerings, it is crucial to provide a seamless and intuitive search experience for our customers, employees, and stakeholders. The Semantic Search Project aims to address these challenges by leveraging AI-driven semantic search technology to elevate our information retrieval system.

The Problem

The current search system faces several limitations, including:

• Lack of Contextual Understanding: The existing search engine primarily relies on keyword matching, resulting in a limited contextual understanding of user queries. As a result, the search results often fail to capture the user's intent accurately
• Inaccurate and Irrelevant Results: Keyword-based searches can produce irrelevant or partially relevant results, leading to user frustration and diminished trust in the search functionality.

Goal of the Project

The Omdena Jordan Chapter team's goal is to create a powerful semantic search algorithm that comprehends user intent and context, enhancing search accuracy and relevance. They will design a user-friendly interface, optimize performance, and uphold ethical AI practices. Leveraging GPT-3 or any newer versions available, they will generate data to build the semantic search system

Project Timeline

What you'll learn

1. Use GPT for collecting data.

2. Learn NLP techniques.

3. Deploy the interface using Streamlit.