Welcome to the Information Retrieval repository! This repository contains code and resources for both search engines and recommender systems. Whether you're interested in building search solutions or recommendation algorithms, you'll find valuable content here.
In the "search" folder, you'll find code and materials related to building and optimizing search engines. This section is perfect for those looking to develop search functionality for their applications, websites, or research projects. Explore the contents of this folder to discover:
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Search Engine Implementations: Code for various search engine implementations, including indexing, query processing, and retrieval.
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Information Retrieval Algorithms: Algorithms and techniques used in information retrieval, such as vector space models, TF-IDF, and more.
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Query Optimization: Resources for improving query performance and relevance.
The "recommend" folder is dedicated to recommender systems. Recommender systems are crucial for suggesting content, products, or services to users. Dive into this section to find:
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Recommender Algorithms: Implementations of recommendation algorithms like collaborative filtering, content-based filtering, and hybrid methods.
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Data Preprocessing: Code for preparing and cleaning data for recommendation tasks.
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Evaluation Metrics: Tools and scripts for evaluating the performance of your recommender systems.
To get started with this repository, follow these steps:
- Clone the repository to your local machine.
git clone https://github.com/Barager/information-retrieval.git