Metadata-Version: 2.1
Name: recommend-py
Version: 0.1.5
Summary: A library to do recommendations based on matrix
Home-page: https://github.com/blacker-sd/pyrecommendation
Author: Ziqian Wang
Author-email: 1793982387@qq.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn

# Recommend_py

## Description
this is a library to do recommendations based on matrix

## Functions
- Function 1: is_utility(df) input the matrix and return if it is a utility matrix
- Function 2: to_utility(df, id_col_num) input the matrix and the column number of the id and return the utility matrix
- Function 3: utility_normalize(df) input the utility matrix and return the normalized utility matrix
- Function 4: utility_standardize(df) input the utility matrix and return the standardized utility matrix
- Function 5: similarity_calculation(df, mean_same_item) input the utility matrix and the minimum number of same items and return the similarity matrix
- Function 6: recommend_calculation(df, top_n, mean_score) input the utility matrix, the number of top items to recommend and the minimum score a similar user must give for an item to be recommended and return the recommendation matrix

## Installation
To install this project, clone the repository and install the required packages:
```
pip install pandas
pip install numpy
pip install scikit-learn
```

## Usage
To use this project, import the library and call the functions:
```
import recommend_py

recommend_py.is_utility(df)
recommend_py.to_utility(df, id_col_num)
recommend_py.utility_normalize(df)
recommend_py.utility_standardize(df)
recommend_py.similarity_calculation(df, mean_same_item)
recommend_py.recommend_calculation(df, top_n, mean_score)
```

