No user intervention is required; the ODBC wrapper automatically recognizes that it is accessing a CF source and changes its behavior accordingly.
这里不需要用户的干预。ODBC包装器自动认识到它正在访问一个CF数据源，并相应地改变它的行为。
Collaborative filtering (CF) is a technique, popularized by Amazon and others, that USES user information such as ratings, clicks, and purchases to provide recommendations to other site users.
协作筛选(CF)是Amazon等公司极为推崇的一项技巧，它使用评分、单击和购买等用户信息为其他站点用户提供推荐产品。
Given a set of users and items, CF applications provide recommendations to the current user of the system. Four ways of generating recommendations are typical.
CF应用程序根据用户和项目历史向系统的当前用户提供推荐。
The second aspect of the (CF) system collects information on what kind of content and commentary you like and dislike, and based on your submission and voting habits, it does user-data-profiling.
协同过滤系统的第二个效果是收集的信息是基于哪种内容、你喜欢还是不喜欢的评注，并根据您提交并参加投票的习惯，这些正是用户数据概况。
To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade (PRM-UG-CF) is presented.
针对传统协同过滤推荐算法的稀疏性、扩展性问题，提出了结合似然关系模型和用户等级的协同过滤推荐算法。
To address these problems, a collaborative filtering recommendation algorithm combining probabilistic relational models and user grade (PRM-UG-CF) is presented.
针对传统协同过滤推荐算法的稀疏性、扩展性问题，提出了结合似然关系模型和用户等级的协同过滤推荐算法。
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