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En vedette
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互联网中推荐系统介绍
1.
联系方式 : [email_address]
2.
什么 是推荐系统为什么 要
推荐系统
3.
推荐系统 和你的 兴趣
不期而遇
4.
推荐系统在互联网中
5.
第一百 只猴子
6.
系统自动推荐的东西还是不能 令人满意 呢
7.
一个成功的 resys 推荐系统的影响权重:
UI/UE 40% , data:30%, knowledge: 20, algorithm:10% 。 百度科学家 张栋
8.
菜市场和高级商场 VS 目的明确
爱逛 , 发现新东西
9.
内容 贫乏 ,只想依靠算法
功能单一,难以满足用户 用户可以找到自己需要的内容
10.
学术界和现实系统的差距 学术界 现实系统
11.
物品 DNA 难以获取
12.
推荐系统中的算法 1. 关联规则
2. 聚类 / 分类 3. SVD 4. 过滤
13.
物品 DNA-- 标签
(tag) 的魅力
14.
做推荐是在一条没有终结的马路上一边攒车一边开车,教科书、 论文里的东西都是基本的零件,中间碰到各种奇奇怪怪的算法 都可能被捡起来装到车上。
豆瓣 CEO 杨勃 ( 阿北 ) 综合算法应用
15.
一切都在细节 1. 算法简约
2. 数据的一些优化 3. 。。。。
16.
评价标准 Normalized Discounted
Cumulative Gain ( NDCG ) 其中:
17.
参考资源: ReSysChina :
http://www.resyschina.com/ 智能推荐系统: http://www.slideshare.net/fuchaoqun/ss-1451423 如何做好推荐系统: http://www.slideshare.net/QinyuanFeng/ss-8477919 动态推荐系统关键技术研究: http://www.slideshare.net/xlvector/ss-8074122 智能 Web 算法: http://book.douban.com/subject/6545083/
18.
感谢欣赏
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