Collaborative Filtering is a technique used by some recommender systems.
This repository is the Python implementation of Collaborative Filtering.
Run:
> python main.py
Notice:
Python Version: 3.5.1
Required modules: Numpy, Pandas, Matplotlib
Need to download the dataset first and put it in the dataset/ folder.
Or, you can see the result without downloading the dataset.
MovieLens, 100K dataset
推荐系统的协同过滤算法实现和浅析 is the pdf version of report.
Python files:
├── main.py # Main python file including training and testing.
├── predict.py # Predict functions.
├── utils.py # Some useful functions, including calculating.
├── var.py # Define global variables.
Jupyter Notebook files:
├── Cross Validation.ipynb # Main file of Collartive Filtering using Cross Validation.
├── TopK.ipynb # File to choose K in Top-K algorithm.
├── alpha.ipynb # File to choose alpha in model blending.
├── MovieLens.ipynb # Early version file for data cleanning.
├── CF.ipynb # Early version file about Collarative Filtering.
Others:
├── LICENSE # MIT LICENSE
├── papers # ignored. Papers have been cited in report.
├── dataset # ignored. You can get it from GroupLens Website.
│ ├── ml-100k # 100K MovieLens dataset
├── report
│ ├── Report.tex # Raw Tex file. Using XeTeX as the engine.
│ ├── Report.bib # References.
│ ├── Report.pdf # Exported pdf report.
│ ├── Plot # Plot folder. Including Echarts.
│ ├── K-figure.png # Pictures included in the report.
│ ├── alpha-figure.png
│ └── rating-pie.png
2016.6