qcymkxyc / Recsys
项亮的《推荐系统实践》的代码实现
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[电影推荐系统] Based on the movie scoring data set, the movie recommendation system is built with FM and LR as the core(基于爬取的电影评分数据集，构建以FM和LR为核心的电影推荐系统).
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RecSys
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另附sklearn官网所有example在线演示：http://www.ailearndo.com
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另这是本人工作之余对《数字图像处理》（冈萨雷斯，第三版）的实现，欢迎Star：https://github.com/qcymkxyc/Image-Process
项亮的《推荐系统实践》的代码实现以及结果展示分析，所有结果见:
测试用例见:
第一章
第一章主要介绍一些推荐系统的评价指标
第二章
第二章介绍推荐系统一些基本的模型。这里实验的数据同书上用MovieLen数据集。整个第二章实验包括前半部分的流行度分析以及后半部分基于MovieLen的推荐算法(协同过滤第一次运算会生成协同矩阵，会比较慢):
第三章
第三章主要讲冷启动问题：
- 用户冷启动
- 根据用户信息特征分组推荐
- 外站信息导入
- 根据用户首次进入反馈的兴趣点
- 物品冷启动
- 基于物品内容信息提取
- 人工标注信息
第四章
介绍基于UGC的推荐。数据集用Delicious数据集(对于冷启动问题推荐热门商品)。
第五章
主要讲时间上下文的推荐算法
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