MovieLens-Recommender is a pure Python implement of Collaborative Filtering. Which contains User Based Collaborative Filtering(UserCF) and Item Based Collaborative Filtering(ItemCF). As comparisons, Random Based Recommendation and Most-Popular Based Recommendation are also included. The famous Latent Factor Model(LFM) is added in this Repo,too.
The buildin-datasets are Movielens-1M and Movielens-100k. But of course, you can use other custom datasets.
Besides, there are two models named UserCF-IIF and ItemCF-IUF, which have improvement to UseCF and ItemCF. They eliminate the influence of very popular users or items.
The book 《推荐系统实践》 written by Xiang Liang is quite wonderful for those people who don't have much knowledge about Recommendation System. But the book only offers each function's implement of Collaborative Filtering. A good architecture project with datasets-build and model-validation process are required.
So I made MovieLens-Recommender project, which is a pure Python implement of Collaborative Filtering based on the ideas of the book.
This repository is based on MovieLens-RecSys, which is also a good implement of Collaborative Filtering. But its efficiency is so damn poor!
Besides, Surprise is a very popular Python scikit building and analyzing recommender systems. So, I Mix the advantages of these two projects, and here comes MovieLens-Recommender.
My Recommendation System contains four steps:
- Create trainset and testset
- Train a recommender model
- Give recommendations
- Evaluate results
At the end of a recommendation process, four numbers are given to measure the recommendation model, which are:
- Precision
- Recall
- Coverage
- Popularity
No python extensions(e.g. Numpy/pandas) are needed!
1. Download
Git is awesome~
git clone https://github.com/fuxuemingzhu/MovieLens-Recommender.gitMovielens-1M and Movielens-100k datasets are under the data/ folder.
2. Run
The configures are in main.py. Pleas choose the dataset and model you want to use and set the proper test_size. The default values in main.py are shown below:
dataset_name = 'ml-100k'
# dataset_name = 'ml-1m'
# model_type = 'UserCF'
# model_type = 'UserCF-IIF'
# model_type = 'ItemCF'
# model_type = 'Random'
# model_type = 'MostPopular'
model_type = 'ItemCF-IUF'
# model_type = 'LFM'
test_size = 0.1Then run python main.py in your command line. There will be a recommendation model built on the dataset you choose above.
Note: my code only tested on python3, so python3 is prefer.
Python main.py
#Python3 main.pyif you are using Linux, this command will redirect the whole output into a file.
Python main.py > run.log 2>&1 &
#Python3 main.py > run.log 2>&1 &This command will run in background. You can wait for the result, or use tail -f run.log to see the real time result.
All model will be saved to model/ fold, which means the time will be cut down in your next run.
3. Output
Here is a example run result of ItemCF model trained on ml-1m with test_size = 0.10. No mater which model are chosen, the output log will like this.
**********************************************************************
This is ItemCF model trained on ml-1m with test_size = 0.10
**********************************************************************
ItemBasedCF start...
No model saved before.
Train a new model...
counting movies number and popularity...
counting movies number and popularity success.
total movie number = 3693
generate items co-rated similarity matrix...
steps(0), 0.00 seconds have spent..
steps(1000), 18.50 seconds have spent..
steps(2000), 46.39 seconds have spent..
steps(3000), 63.52 seconds have spent..
steps(4000), 87.37 seconds have spent..
steps(5000), 111.83 seconds have spent..
steps(6000), 132.71 seconds have spent..
generate items co-rated similarity matrix success.
total step number is 6040
total 133.61 seconds have spent
calculate item-item similarity matrix...
steps(0), 0.00 seconds have spent..
steps(1000), 1.77 seconds have spent..
steps(2000), 3.47 seconds have spent..
steps(3000), 5.01 seconds have spent..
calculate item-item similarity matrix success.
total step number is 3693
total 5.67 seconds have spent
Train a new model success.
The new model has saved success.
recommend for userid = 1:
['1196', '364', '1265', '318', '2081', '1282', '1198', '2716', '1', '2096']
recommend for userid = 100:
['2916', '1580', '457', '1240', '589', '1291', '780', '1036', '1610', '1214']
recommend for userid = 233:
['1022', '594', '1282', '2087', '2078', '1196', '608', '2081', '593', '1393']
recommend for userid = 666:
['296', '1704', '593', '356', '1196', '589', '1580', '50', '1393', '1']
recommend for userid = 888:
['2916', '457', '480', '2628', '1265', '1610', '1198', '1573', '2762', '1527']
Test recommendation system start...
steps(0), 0.10 seconds have spent..
steps(1000), 291.42 seconds have spent..
steps(2000), 627.60 seconds have spent..
steps(3000), 898.21 seconds have spent..
steps(4000), 1219.94 seconds have spent..
steps(5000), 1523.29 seconds have spent..
steps(6000), 1817.46 seconds have spent..
Test recommendation system success.
total step number is 6040
total 1829.26 seconds have spent
precision=0.1900 recall=0.1147 coverage=0.1673 popularity=7.3911
total Main Function step number is 0
total 1972.49 seconds have spent
Here are four models' benchmarks over Precision、Recall、Coverage、Popularity. The testsize is 0.1.
These results are nearly same with Xiang Liang's book, which proves that my algorithms are right.
Movielens 1M:
| Movielens 1M | Precision | Recall | Coverage | Popularity |
|---|---|---|---|---|
| UserCF | 19.84% | 11.97% | 28.16% | 7.2023 |
| ItemCF | 19.00% | 11.47% | 16.73% | 7.3911 |
| UserCF-IIF | 19.77% | 11.93% | 29.62% | 7.1660 |
| ItemCF-IUF | 18.71% | 11.29% | 15.03% | 7.4748 |
| LFM | / | / | / | / |
| Random | 0.54% | 0.33% | 100.00% | 4.4075 |
| Most Popular | 10.59% | 6.39% | 2.76% | 7.7462 |
Movielens 100k:
| Movielens 100k | Precision | Recall | Coverage | Popularity |
|---|---|---|---|---|
| UserCF | 19.69% | 18.50% | 22.20% | 5.4928 |
| ItemCF | 17.89% | 16.80% | 13.23% | 5.6202 |
| UserCF-IIF | 19.57% | 18.38% | 22.74% | 5.4716 |
| ItemCF-IUF | 20.38% | 12.30% | 17.30% | 7.3643 |
| LFM | 20.29% | 19.06% | 27.41% | 4.9983 |
| Random | 0.82% | 0.77% | 99.64% | 3.0332 |
| Most Popular | 10.54% | 9.90% | 4.07% | 5.9578 |
UserCF is faser than ItemCF. Using ml-100k instead of ml-1m will speed up the predict process.
Caculating similarity matrix is quite slow. Please wait for the result patiently.
LFM will make negative samples when running. And when the ratio of Neg./Pos. goes to larger, the performance goes to better.
LFM has more parameters to tune, and I don't spend much time to do this. I believe you will do quite better!
Apache License.
Copyright 2018 fuxuemingzhu
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.