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。
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。
Our algorithm, item-to-item collaborative filtering, scales to massive data sets and produces high-quality recommendations in real time.
我们的算法，也就是商品到商品的协同过滤，符合海量的数据集和产品量，并能实时得到高品质的推荐。
Amazon (AMZN) calls this homegrown math "item-to-item collaborative filtering," and it's used this algorithm to heavily customize the browsing experience for returning customers.
亚马逊把这套自主研发的算法称为“从项目到项目的协同过滤算法”。依靠这套算法，亚马逊向回头客们提供了深度定制的浏览体验。
To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm. In this paper we propose a new method based item matrix partition.
为了有效地解决协同过滤算法中新项目难以推荐的问题，文中提出了一种对项目矩阵进行划分的方法。
To efficiently resolve the problem that the new item is difficult to recommend in collaborative filtering algorithm. In this paper we propose a new method based item matrix partition.
为了有效地解决协同过滤算法中新项目难以推荐的问题，文中提出了一种对项目矩阵进行划分的方法。
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