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https://github.com/lujiaying/MovieTaster-Open
A practical movie recommend project based on Item2vec.
https://github.com/lujiaying/MovieTaster-Open
deep-learning item2vec word2vec
Last synced: 3 months ago
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A practical movie recommend project based on Item2vec.
- Host: GitHub
- URL: https://github.com/lujiaying/MovieTaster-Open
- Owner: lujiaying
- License: other
- Created: 2017-08-14T08:45:56.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-10-12T12:20:41.000Z (about 4 years ago)
- Last Synced: 2024-05-15T04:37:20.302Z (6 months ago)
- Topics: deep-learning, item2vec, word2vec
- Language: Python
- Homepage:
- Size: 15 MB
- Stars: 278
- Watchers: 12
- Forks: 91
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MovieTaster-Open
A movie recommend project based on Item2vec.
Reference:
- Barkan, Oren, and Noam Koenigstein. "Item2vec: neural item embedding for collaborative filtering." Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on. IEEE, 2016.
- JayveeHe, https://github.com/JayveeHe/MusicTaster. Github.
[Demo>](https://movietaster.leanapp.cn/movies/)
More details for this project, plese refer to [blog>](https://lujiaying.github.io/posts/2017/08/MovieTaster/) or [zhihu>](https://zhuanlan.zhihu.com/p/28491088)
## Project Struct
- datas: to store corpus
- models: to store item vectors for movies
- utils: a collection of tool functions
## Usage
0. Compile [fasttext](https://github.com/facebookresearch/fastText) under root of the project
1. Process corpus, the data file would be generated under ```./datas/```.
```
$ cd datas && tar -xzvf corpus.tar.gz
$ cd ..
$ python utils/process.py
```
2. Train model by running fasttext. Please refer to ```./run.sh``` for other configuration.
```
$ ./fasttext skipgram -input ./datas/doulist_0804_09.movie_id -output ./models/fasttext_model_0804_09_skipgram -minCount 5 -epoch 50 -neg 100
```
3. Here's a result from the default configuration.
```
$ python utils/eval.py
movie_name_id_dict is 130206 from [./datas/doulist_0804_09.json]
movie_name_id_dict is 130206 from [./datas/doulist_0804_09.json]
movie_id_name_dict is 130206 from [./datas/doulist_0804_09.json]
[2017-08-18 07:52:53,579][pid:22831] eval.topk_like: INFO: [1210]小时代 top 5 likes:
[2017-08-18 07:52:54,149][pid:22831] eval.topk_like: INFO: [2396]小时代2：青木时代 0.820323
[2017-08-18 07:52:54,149][pid:22831] eval.topk_like: INFO: [3387]分手合约 0.606063
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [4087]不二神探 0.604207
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [3839]天台爱情 0.601879
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [821]致我们终将逝去的青春 0.600836
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [144]倩女幽魂 top 5 likes:
[2017-08-18 07:52:54,690][pid:22831] eval.topk_like: INFO: [2719]倩女幽魂3：道道道 0.685621
[2017-08-18 07:52:54,690][pid:22831] eval.topk_like: INFO: [1812]倩女幽魂2：人间道 0.681594
[2017-08-18 07:52:54,691][pid:22831] eval.topk_like: INFO: [466]胭脂扣 0.678032
[2017-08-18 07:52:54,691][pid:22831] eval.topk_like: INFO: [261]青蛇 0.671541
[2017-08-18 07:52:54,692][pid:22831] eval.topk_like: INFO: [156]东邪西毒 0.664057
[2017-08-18 07:52:54,692][pid:22831] eval.topk_like: INFO: [2602]悟空传 top 5 likes:
[2017-08-18 07:52:55,253][pid:22831] eval.topk_like: INFO: [5648]闪光少女 0.671337
[2017-08-18 07:52:55,253][pid:22831] eval.topk_like: INFO: [2189]绣春刀II：修罗战场 0.646861
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [8297]逆时营救 0.634753
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [12571]京城81号Ⅱ 0.625549
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [10545]父子雄兵 0.623032
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [56]美国往事 top 5 likes:
[2017-08-18 07:52:55,789][pid:22831] eval.topk_like: INFO: [18]天堂电影院 0.756449
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [14]辛德勒的名单 0.737502
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [17]教父 0.735216
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [69]闻香识女人 0.734119
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [42]西西里的美丽传说 0.732334
[2017-08-18 07:52:55,791][pid:22831] eval.topk_like: INFO: [2644]战狼2 top 5 likes:
[2017-08-18 07:52:56,346][pid:22831] eval.topk_like: INFO: [1456]大护法 0.612700
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [2107]战狼 0.580637
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [2602]悟空传 0.580365
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [9941]建军大业 0.575422
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [19040]阿唐奇遇 0.573613
```
------------------------
# MovieTaster-Open
使用Item2Vec做电影推荐
参考:
- Barkan, Oren, and Noam Koenigstein. "Item2vec: neural item embedding for collaborative filtering." Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on. IEEE, 2016.
- JayveeHe, https://github.com/JayveeHe/MusicTaster. Github.
[Demo>](https://movietaster.leanapp.cn/movies/)
[原理详情请参考>](https://lujiaying.github.io/posts/2017/08/MovieTaster/)
## 目录结构
- datas: 存放语料
- models: 存放电影向量
- utils: 工具类，包括生成推荐电影列表等
## 使用说明
0. 在根目录下载并编译[fasttext](https://github.com/facebookresearch/fastText)
1. 处理语料。结束后将在```./datas/```下生成fasttext所需要的格式
```
$ cd datas && tar -xzvf corpus.tar.gz
$ cd ..
$ python utils/process.py
```
2. 参考```./run.sh```中生成词向量的参数配置，任选一个。
```
$ ./fasttext skipgram -input ./datas/doulist_0804_09.movie_id -output ./models/fasttext_model_0804_09_skipgram -minCount 5 -epoch 50 -neg 100
```
3. 查看模型效果
```
$ python utils/eval.py
movie_name_id_dict is 130206 from [./datas/doulist_0804_09.json]
movie_name_id_dict is 130206 from [./datas/doulist_0804_09.json]
movie_id_name_dict is 130206 from [./datas/doulist_0804_09.json]
[2017-08-18 07:52:53,579][pid:22831] eval.topk_like: INFO: [1210]小时代 top 5 likes:
[2017-08-18 07:52:54,149][pid:22831] eval.topk_like: INFO: [2396]小时代2：青木时代 0.820323
[2017-08-18 07:52:54,149][pid:22831] eval.topk_like: INFO: [3387]分手合约 0.606063
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [4087]不二神探 0.604207
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [3839]天台爱情 0.601879
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [821]致我们终将逝去的青春 0.600836
[2017-08-18 07:52:54,150][pid:22831] eval.topk_like: INFO: [144]倩女幽魂 top 5 likes:
[2017-08-18 07:52:54,690][pid:22831] eval.topk_like: INFO: [2719]倩女幽魂3：道道道 0.685621
[2017-08-18 07:52:54,690][pid:22831] eval.topk_like: INFO: [1812]倩女幽魂2：人间道 0.681594
[2017-08-18 07:52:54,691][pid:22831] eval.topk_like: INFO: [466]胭脂扣 0.678032
[2017-08-18 07:52:54,691][pid:22831] eval.topk_like: INFO: [261]青蛇 0.671541
[2017-08-18 07:52:54,692][pid:22831] eval.topk_like: INFO: [156]东邪西毒 0.664057
[2017-08-18 07:52:54,692][pid:22831] eval.topk_like: INFO: [2602]悟空传 top 5 likes:
[2017-08-18 07:52:55,253][pid:22831] eval.topk_like: INFO: [5648]闪光少女 0.671337
[2017-08-18 07:52:55,253][pid:22831] eval.topk_like: INFO: [2189]绣春刀II：修罗战场 0.646861
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [8297]逆时营救 0.634753
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [12571]京城81号Ⅱ 0.625549
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [10545]父子雄兵 0.623032
[2017-08-18 07:52:55,254][pid:22831] eval.topk_like: INFO: [56]美国往事 top 5 likes:
[2017-08-18 07:52:55,789][pid:22831] eval.topk_like: INFO: [18]天堂电影院 0.756449
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [14]辛德勒的名单 0.737502
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [17]教父 0.735216
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [69]闻香识女人 0.734119
[2017-08-18 07:52:55,790][pid:22831] eval.topk_like: INFO: [42]西西里的美丽传说 0.732334
[2017-08-18 07:52:55,791][pid:22831] eval.topk_like: INFO: [2644]战狼2 top 5 likes:
[2017-08-18 07:52:56,346][pid:22831] eval.topk_like: INFO: [1456]大护法 0.612700
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [2107]战狼 0.580637
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [2602]悟空传 0.580365
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [9941]建军大业 0.575422
[2017-08-18 07:52:56,347][pid:22831] eval.topk_like: INFO: [19040]阿唐奇遇 0.573613
```