Semantic Book Recommendation System -
This project implements a semantic book recommendation system that suggests books based on the meaning of a user's query rather than simple keyword matching.
Traditional recommendation systems rely on exact words. This system uses Natural Language Processing (NLP) techniques to understand the context of a query and recommend books with similar descriptions.
Example queries:
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"dark fantasy with dragons"
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"romantic story set in Europe"
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"books similar to Harry Potter"
The system converts book descriptions and user queries into vector representations and then finds books with the highest similarity scores.
Tech Stack
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Python
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Pandas
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NumPy
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Scikit-learn
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NLP text vectorization (TF-IDF)
Installation
Clone the repository:
git clone https://github.com/Shadow-code-dev/Semantic-Book-Recommendation-System.git cd Semantic-Book-Recommendation-System
Install dependencies:
pip install -r requirements.txt