| « | September 2025 | » | | 日 | 一 | 二 | 三 | 四 | 五 | 六 | | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | | | | | |
|
公告 |
| 暂无公告... |
| Blog信息 |
|
blog名称:
日志总数:111
评论数量:190
留言数量:-24
访问次数:639542
建立时间:2007年4月21日 |
| |
|
 [推荐系统]Browsing and Recommendations
网上资源
赵勇 发表于 2007/4/23 10:15:50 |
|
Browsing and Recommendations
http://www.readwriteweb.com/archives/recommendation_engines.phpA good recommendation engine can make a difference not just for Netflix, but for any online business. This is because there are two fundamental activities online - Search and Browse. When a consumer knows exactly what she is looking for, she searches for it. But when she is not looking for anything specific, she browses. It is the browsing that holds the golden opportunity for a recommendation system, because the user is not focused on finding a specific thing - she is open to suggestions.
During browsing, the user's attention (and their money) is up for grabs. By showing the user something compelling, a web site maximizes the likelihood of a transaction. So if a web site can increase the chances of giving users good recommendations, it makes more money. Obviously this is a difficult problem, but the incentive to solve it is very big. The main approaches fall into the following categories:
Personalized recommendation - recommend things based on the individual's past behavior
Social recommendation - recommend things based on the past behavior of similar users
Item recommendation - recommend things based on the thing itself
A combination of the three approaches above |
|
|
回复:Browsing and Recommendations
网上资源
漂流发表评论于2007/4/23 10:36:10 |
|
» 1 »
|