# AIFuzzyRec
A Python library for generating AI-powered item recommendations using fuzzy logic and SQLite databases. AIFuzzyRec leverages fuzzy set theory and artificial intelligence to compute item similarities based on user-item interactions, providing flexible and AI-enhanced recommendations for applications like e-commerce, content platforms, or personalized systems.
## Features
- Fuzzy Logic-Based Recommendations: Uses fuzzy membership functions to compute item similarities, allowing nuanced recommendation scores.
- Flexible Database Schema: Supports any SQLite database with user-item interaction data, with customizable table and column names.
- Context Manager Support: Ensures automatic cleanup of database connections using Python's with statement.
- Configurable Fuzzy Weights: Adjust weights for low, medium, and high similarity to fine-tune recommendation behavior.
- Error Handling: Robust handling of invalid user/item IDs and empty databases.
- Logging: Optional logging of recommendations to a file for debugging and analysis.
- Easy Integration: Lightweight and compatible with Python 3.6+.
## Installation
Install AIFuzzyRec using pip:
```
pip install ai-fuzzy-rec
```
Or, if using Poetry, add it to your project:
```
poetry add ai-fuzzy-rec
```
### Requirements
Python >= 3.6
pandas >= 2.0.0
scikit-fuzzy >= 0.4.2
numpy >= 1.25.0
scipy >= 1.10.0
packaging >= 21.3
## Usage
### Basic Example
Generate recommendations using a SQLite database (purchases.db) containing user-item interactions:
```
from ai_fuzzy_rec import FuzzyRecommender
# Initialize with default settings
with FuzzyRecommender(db_path='purchases.db') as recommender:
# Recommend items similar to item ID 2
item_rec = recommender.recommend_items_for_item(2, top_n=3)
print(f"Items recommended for item 2: {[(item, round(score, 3)) for item, score in item_rec]}")
# Recommend items for user ID 1
user_rec = recommender.recommend_items_for_user(1, top_n=3)
print(f"Items recommended for user 1: {[(item, round(score, 3)) for item, score in user_rec]}")
```
Expected Output:
```
Items recommended for item 2: [(3, 0.5), (4, 0.5), (1, 0.5)]
Items recommended for user 1: [(4, 1.0), (5, 0.5)]
```
### Custom Schema and Parameters
Use a custom database schema and adjust fuzzy weights:
```
from ai_fuzzy_rec import FuzzyRecommender
with FuzzyRecommender(
db_path='custom.db',
table_name='orders',
user_col='customer_id',
item_col='product_id',
fuzzy_weights=(0.1, 0.4, 0.5), # Emphasize high similarity
log_file='custom_recommendations.log'
) as recommender:
item_rec = recommender.recommend_items_for_item(2, top_n=2)
print(f"Top 2 items for item 2: {item_rec}")
```
### Database Setup
Create a SQLite database with user-item interactions:
```
import sqlite3
# Create a sample database
conn = sqlite3.connect('purchases.db')
cursor = conn.cursor()
cursor.execute('''CREATE TABLE IF NOT EXISTS purchases
(user_id INTEGER, item_id INTEGER)''')
# Insert sample data
data = [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (2, 4),
(3, 3), (3, 4), (3, 5), (4, 1), (4, 4), (5, 1), (5, 2)]
cursor.executemany('INSERT INTO purchases VALUES (?, ?)', data)
conn.commit()
conn.close()
```
### Testing
Run tests to verify functionality:
```
poetry run pytest
```
This executes tests in tests/test_ai_fuzzy_rec.py, ensuring recommendations and error handling work as expected.
### Project Structure
```
/home/m/pyprj/
├── ai_fuzzy_rec/
│ ├── __init__.py
│ ├── ai_fuzzy_rec.py
├── create_database.py
├── purchases.db
├── pyproject.toml
├── README.md
├── LICENSE
├── test_recommender.py
└── tests/
└── test_ai_fuzzy_rec.py
```
Raw data
{
"_id": null,
"home_page": "https://github.com/fadedreams/ai-fuzzy-rec",
"name": "ai-fuzzy-rec",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.6",
"maintainer_email": null,
"keywords": "fuzzy logic, recommender system, ai, sqlite, recommendations",
"author": "fadedreams7",
"author_email": "fadedreams7@gmail.com",
"download_url": "https://files.pythonhosted.org/packages/2a/77/5045383c3b835590c71d506dea27741c78aba13d1beb53750d3b6928ac1f/ai_fuzzy_rec-0.1.0.tar.gz",
"platform": null,
"description": "# AIFuzzyRec\n\nA Python library for generating AI-powered item recommendations using fuzzy logic and SQLite databases. AIFuzzyRec leverages fuzzy set theory and artificial intelligence to compute item similarities based on user-item interactions, providing flexible and AI-enhanced recommendations for applications like e-commerce, content platforms, or personalized systems.\n\n## Features\n- Fuzzy Logic-Based Recommendations: Uses fuzzy membership functions to compute item similarities, allowing nuanced recommendation scores.\n\n- Flexible Database Schema: Supports any SQLite database with user-item interaction data, with customizable table and column names.\n\n- Context Manager Support: Ensures automatic cleanup of database connections using Python's with statement.\n\n- Configurable Fuzzy Weights: Adjust weights for low, medium, and high similarity to fine-tune recommendation behavior.\n\n- Error Handling: Robust handling of invalid user/item IDs and empty databases.\n\n- Logging: Optional logging of recommendations to a file for debugging and analysis.\n\n- Easy Integration: Lightweight and compatible with Python 3.6+.\n\n## Installation\n\nInstall AIFuzzyRec using pip:\n```\npip install ai-fuzzy-rec\n```\nOr, if using Poetry, add it to your project:\n```\npoetry add ai-fuzzy-rec\n```\n### Requirements\nPython >= 3.6\npandas >= 2.0.0\nscikit-fuzzy >= 0.4.2\nnumpy >= 1.25.0\nscipy >= 1.10.0\npackaging >= 21.3\n\n## Usage\n\n### Basic Example\n\nGenerate recommendations using a SQLite database (purchases.db) containing user-item interactions:\n```\nfrom ai_fuzzy_rec import FuzzyRecommender\n\n# Initialize with default settings\nwith FuzzyRecommender(db_path='purchases.db') as recommender:\n # Recommend items similar to item ID 2\n item_rec = recommender.recommend_items_for_item(2, top_n=3)\n print(f\"Items recommended for item 2: {[(item, round(score, 3)) for item, score in item_rec]}\")\n\n # Recommend items for user ID 1\n user_rec = recommender.recommend_items_for_user(1, top_n=3)\n print(f\"Items recommended for user 1: {[(item, round(score, 3)) for item, score in user_rec]}\")\n```\nExpected Output:\n```\nItems recommended for item 2: [(3, 0.5), (4, 0.5), (1, 0.5)]\nItems recommended for user 1: [(4, 1.0), (5, 0.5)]\n```\n### Custom Schema and Parameters\n\nUse a custom database schema and adjust fuzzy weights:\n```\nfrom ai_fuzzy_rec import FuzzyRecommender\n\nwith FuzzyRecommender(\n db_path='custom.db',\n table_name='orders',\n user_col='customer_id',\n item_col='product_id',\n fuzzy_weights=(0.1, 0.4, 0.5), # Emphasize high similarity\n log_file='custom_recommendations.log'\n) as recommender:\n item_rec = recommender.recommend_items_for_item(2, top_n=2)\n print(f\"Top 2 items for item 2: {item_rec}\")\n```\n### Database Setup\n\nCreate a SQLite database with user-item interactions:\n```\nimport sqlite3\n\n# Create a sample database\nconn = sqlite3.connect('purchases.db')\ncursor = conn.cursor()\ncursor.execute('''CREATE TABLE IF NOT EXISTS purchases\n (user_id INTEGER, item_id INTEGER)''')\n# Insert sample data\ndata = [(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (2, 4),\n (3, 3), (3, 4), (3, 5), (4, 1), (4, 4), (5, 1), (5, 2)]\ncursor.executemany('INSERT INTO purchases VALUES (?, ?)', data)\nconn.commit()\nconn.close()\n```\n### Testing\n\nRun tests to verify functionality:\n```\npoetry run pytest\n```\nThis executes tests in tests/test_ai_fuzzy_rec.py, ensuring recommendations and error handling work as expected.\n\n### Project Structure\n```\n/home/m/pyprj/\n\u251c\u2500\u2500 ai_fuzzy_rec/\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u251c\u2500\u2500 ai_fuzzy_rec.py\n\u251c\u2500\u2500 create_database.py\n\u251c\u2500\u2500 purchases.db\n\u251c\u2500\u2500 pyproject.toml\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 test_recommender.py\n\u2514\u2500\u2500 tests/\n \u2514\u2500\u2500 test_ai_fuzzy_rec.py\n\n```\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "An AI-enhanced fuzzy logic-based item recommendation system for SQLite databases.",
"version": "0.1.0",
"project_urls": {
"Homepage": "https://github.com/fadedreams/ai-fuzzy-rec",
"Repository": "https://github.com/fadedreams/ai-fuzzy-rec"
},
"split_keywords": [
"fuzzy logic",
" recommender system",
" ai",
" sqlite",
" recommendations"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "833f542e244ef33eb5357b1d77c7cabce456d9c1e66bb629b320b7406a4370bb",
"md5": "91d5620e5f023cde430fd8d115a9eefc",
"sha256": "51e5adc61635a51d5b4dbfbc6320349ac69cfd03e9c3d92d509b0e0e2d4bb0ad"
},
"downloads": -1,
"filename": "ai_fuzzy_rec-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "91d5620e5f023cde430fd8d115a9eefc",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.6",
"size": 6174,
"upload_time": "2025-07-14T09:17:01",
"upload_time_iso_8601": "2025-07-14T09:17:01.859563Z",
"url": "https://files.pythonhosted.org/packages/83/3f/542e244ef33eb5357b1d77c7cabce456d9c1e66bb629b320b7406a4370bb/ai_fuzzy_rec-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "2a775045383c3b835590c71d506dea27741c78aba13d1beb53750d3b6928ac1f",
"md5": "d3192780b7a0dc90d80ce9f6558fcdbe",
"sha256": "7992a4399de8cd1205901cd5c3fbe910c7e3ec1f546c343381f9303606ba8786"
},
"downloads": -1,
"filename": "ai_fuzzy_rec-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "d3192780b7a0dc90d80ce9f6558fcdbe",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.6",
"size": 5292,
"upload_time": "2025-07-14T09:17:03",
"upload_time_iso_8601": "2025-07-14T09:17:03.070108Z",
"url": "https://files.pythonhosted.org/packages/2a/77/5045383c3b835590c71d506dea27741c78aba13d1beb53750d3b6928ac1f/ai_fuzzy_rec-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-14 09:17:03",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "fadedreams",
"github_project": "ai-fuzzy-rec",
"github_not_found": true,
"lcname": "ai-fuzzy-rec"
}
JSON
