https://github.com/anhhuy007/bookwise-model
A sophisticated book recommendation system that combines content-based filtering with Semantic Search for enhanced personalized recommendations.
https://github.com/anhhuy007/bookwise-model
fastapi recommendation-system sentence-transformers
Last synced: 3 months ago
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A sophisticated book recommendation system that combines content-based filtering with Semantic Search for enhanced personalized recommendations.
- Host: GitHub
- URL: https://github.com/anhhuy007/bookwise-model
- Owner: anhhuy007
- Created: 2024-12-31T18:21:22.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-01-07T21:49:41.000Z (over 1 year ago)
- Last Synced: 2025-07-05T18:32:12.133Z (12 months ago)
- Topics: fastapi, recommendation-system, sentence-transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 13.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Book Recommendation Project
This repository contains models to recommend books for users, including:
1. **Demographic Filtering Model:** Recommends books based on aggregated user data, such as popularity and average ratings.
2. **Collaborative Filtering Model:** Identifies users with similar tastes and suggests books they have enjoyed.
3. **Content-Based Filtering Model:** Recommends books that share similar themes or genres with the user's previously read or liked books.
4. **Semantic Search Model:** Leverages Natural Language Processing (NLP) and vector databases to understand the meaning and context of user queries and book descriptions, providing a more refined search experience.
---
## Getting Started
1. **Install FastAPI:**
```bash
pip install "fastapi[standard]"
```
---
## To Run the Project
1. **Run the preprocessing script:**
```bash
python preprocessed_data.py
```
This script will prepare the data for the models.
2. **Run the server with:**
```bash
fastapi dev app.py
```
3. **Access the API:** After launching the server, you can access the interactive API documentation at `http://127.0.0.1:8000/docs` to explore the available endpoints.
---
## For more information
Read our report: [Book Recommendation Report](reports/Book_Recommend.pdf)