基于商品项的推荐系统克服了不少传统用户协作推荐的缺点，但同时放弃了对用户本身兴趣的推荐。
Other systems based on ware item recommendation can overcome shortcomings of the traditional user collaboration recommendation, but at the same time lose recommendation of the user's interests.
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。
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.
无线自组网中信任推荐依赖于节点间协作，而信任系统自身无法为该行为提供信任评价。
Trust recommended behaviors rely on the cooperation among nodes in wireless AD hoc network and trust system itself cannot provide trust evaluations for the behaviors.
协作过滤是应用最为广泛的推荐技术，通常提供预测评分作为推荐。
Collaborative filtering, which is widely used recommendation algorithm, usually provides predicted ratings as recommendation.
提出一种新的协作过滤算法，采用概率形式，即预测用户喜欢商品的概率来推荐。
A new algorithm is proposed, which USES a probability value as the output showing the chance that a user might like an item.
提出一种新的协作过滤算法，采用概率形式，即预测用户喜欢商品的概率来推荐。
A new algorithm is proposed, which USES a probability value as the output showing the chance that a user might like an item.
应用推荐