# MovieTaster-Open **Repository Path**: hhd9497/MovieTaster-Open ## Basic Information - **Project Name**: MovieTaster-Open - **Description**: A practical movie recommend project based on Item2vec. - **Primary Language**: Python - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2019-07-16 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## 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 ```