一篇比较来自朋友的推荐和来自RS推荐的文章,没有实验,没有公式,没有编程。
摘要:选取六个在线推荐系统(三个书籍,三个电影),结果显示朋友提供更好的推荐,但是RS更能提供一些有用的,意想不到的推荐。调查显示用户不介意提供更多的信息去获取更好的推荐。推荐一些用户以前喜欢过的物品会增加用户对系统的信任感。新奇性是传统推荐的任务,而意外性则是推荐发展的一个新方向
假设:相对于RS,用户更喜欢来自朋友的推荐,因为朋友了解用户,知道其喜好(被推翻)
自变量:推荐来源 Item领域 系统本身
评价指标:
1. 好的推荐
实验过程和结果:see detail in paper
设计RS的建议:
1. Users don't mind rating more items initially to receive quality recommendations.
2. Allow users to provide initial ratings on a continuous rather than binary choice scale.
3. Provide enough information about the recommended item for user to make a decision.
4. Provide easy ways to generate new recommendation sets.
5. Interface matters, mostly when it gets in the way.
有一届RecSys会议,一位学者在做KEYNOTE的时候也提出了设计推荐系统的十点建议。
RECSYS 2009 KEYNOTE: TOP 10 LESSONS LEARNED DEVELOPING, DEPLOYING, AND OPERATING REAL-WORLD RECOMMENDER SYSTEMS
Comparing Recommendations Made by Online Systems and Friends Rashmi Sinha and Kirsten Swearingen
下载地址:http://onemvweb.com/sources/sources/comparing_recommendations.pdf
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