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1·Metadata Creation - tagging, curation and collaborative filtering.

元数据生成—标签，管理以及群体过滤。

2·Data sparseness is a serious problem in collaborative filtering system.

数据稀疏性是协同过滤系统面临的一个巨大挑战。

3·Data sparsity problem is a potential challenge of collaborative filtering.

数据稀缺性问题是协同过滤技术面临的主要挑战。

4·This paper puts forward an model based on classify in collaborative filtering.

在协同过滤中，提出基于分类的协同过滤算法。

5·Very few systems now are combining metadata or content with collaborative filtering.

极少有系统把元数据和内容一起用来做协同过滤。

6·All this means that there's a ceiling to how accurate collaborative filtering can get.

所有这一切意味着“协同筛选”永远不会做到尽善尽美。

7·Recommender System; Collaborative Filtering; User Trust; Malicious Attack; Similarity.

推荐系统；协同过滤；用户信任；恶意攻击；相似性。

8·Using collaborative filtering to generate recommendations is computationally expensive.

利用协同过滤来产生推荐，很耗计算。

9·Unlike other algorithms, item-to-item collaborative filtering is able to meet this challenge.

与其他算法不同，商品到商品的协同过滤能满足这样的挑战。

10·Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering.

在此，我们就这些方法与我们的算法——我们称之为商品到商品的协同过滤——进行对比。