基于内容的过滤通过分析目标的内容形成对访客兴趣的陈述。

Content-based filtering works by analyzing the content of the objects to form a representation of the visitor's interests.

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与在目标中找寻过去的相似点的基于内容的过滤不同，协同过滤通过找寻具有相同品位的访客开发出推荐的项目。

Instead of finding objects similar to those a visitor liked in the past, as in content-based filtering, collaborative filtering develops recommendations by finding visitors with similar tastes.

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基于内容的过滤算法大多数是基于向量空间模型的算法，其中广泛使用的是朴素贝叶斯算法和K最近邻（KNN）算法。

Most of the content-based filtering algorithms are based on vector space model, of which Naive Bayes algorithm and K-Nearest Neighbor (KNN) algorithm are widely used.

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