Comparing Recommendations Made by Online Systems and Friends
Comparing Recommendations Made by Online Systems and Friends
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发表时间：
2001
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通讯作者：
R. Sinha;Kirsten Swearingen
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作者：
R. Sinha;Kirsten Swearingen
六个在线推荐系统（RS）的建议和可用性的质量进行了检查。评估了三本书RS（Amazon.com，RatingZone & Sleeper）和三部电影RS（Amazon.com，MovieCritic，Reel.com）。通过比较RS的推荐和用户朋友的推荐来探索推荐的质量。结果显示，用户的朋友始终提供比RS更好的推荐。然而，用户确实发现在线RS推荐的项目很有用：推荐的项目通常是“新的”和“意想不到的”，而朋友推荐的项目大多是提醒以前确定的兴趣。RS的可用性评价表明，用户不介意为系统提供更多的输入，以获得更好的建议。此外，如果一个系统推荐了他们以前喜欢的项目，用户会更信任它。人们决定读什么书的一个常见方法是向朋友和熟人征求建议。这种经过时间考验的方法背后的逻辑是，一个人对书籍、电影、音乐等有共同的品味，和朋友在一起因此，吸引他们（朋友）的项目可能会吸引我。在线推荐系统（RS）试图为这种社会过滤过程创建一个技术代理。许多RS背后的假设是，为用户个性化推荐的一个好方法是识别具有相似兴趣的人，并推荐这些志同道合的人感兴趣的项目（Resnick & Varian（1997），Goldberg，Nichols，Oki & Terry（1992））。这个前提构成了大多数协同过滤算法的统计基础。由于大多数RS的目标是取代（或至少增强）本质上是一个社会过程，我们决定直接比较两种接收推荐的方式（朋友和在线RS）。用户是否喜欢从在线系统接收推荐？在线系统提供的推荐与用户朋友提供的推荐有何不同？我们的假设是，朋友会做出上级推荐，因为他们很了解用户，并且对他/她在许多领域的品味有着深入的了解。相比之下，RS只具有关于用户的有限的、特定于领域的知识。而且，信息检索系统还不能与人类判断过程的复杂性相匹配。
The quality of recommendations and usability of six online recommender systems (RS) was examined. Three book RS (Amazon.com, RatingZone & Sleeper) and three movie RS (Amazon.com, MovieCritic, Reel.com) were evaluated. Quality of recommendations was explored by comparing recommendations made by RS to recommendations made by the user’s friends. Results showed that the user’s friends consistently provided better recommendations than RS. However, users did find items recommended by online RS useful: recommended items were often “new” and “unexpected”, while the items recommended by friends mostly served as reminders of previously identified interests. Usability evaluation of the RS showed that users did not mind providing more input to the system in order to get better recommendations. Also users trusted a system more if it recommended items that they had previously liked. A common way for people to decide what books to read is to ask friends and acquaintances for recommendations. The logic behind this time-tested method is that one shares tastes in books, movies, music etc., with one’s friends. As such, items that appeal to them (friends) might appeal to me. Online Recommender Systems (RS) attempt to create a technological proxy for this social filtering process. The assumption behind many RS is that a good way to personalize recommendations for a user is to identify people with similar interests and recommend items that have interested these like-minded people (Resnick & Varian (1997), Goldberg, Nichols, Oki & Terry (1992)). This premise forms the statistical basis of most collaborative filtering algorithms. Since the goal of most RS is to replace (or at least augment) what is essentially a social process, we decided to directly compare the two ways of receiving recommendations (friends & online RS). Do users like receiving recommendations from an online system? How do the recommendations provided by online systems differ from those provided by the users’ friends? Our hypothesis was that friends would make superior recommendations since they know the user well, and have intimate knowledge of his / her tastes in a number of domains. In contrast, RS only have limited, domain-specific knowledge about the users. Also, information retrieval systems do not yet match the sophistication of human judgment processes.