machine-learning - Is it Item based or content based Collaborative filtering?

Question

I am currently working on an existing system that recommends items that are similar to previous items that the user has liked.

It uses Alternating least squares Collaborative Filtering to find feature vectors of users and items. It then uses the feature vectors of the items and uses the cosine similarity measure to find similar items to it.

However, I would like some clarification as to whether this is item based CF or content based filtering? My inclination is that it is both. Since it is using a similarity measure to compare items, but the items are on the content of the feature vector?

Thanks,

Answer 1

如果我正确理解您从类似用户的项目数据中提取项目的特征向量，那么它是纯基于项目的 CF。

为了成为基于内容的过滤，应该使用项目本身的特征：例如，如果项目是电影，则基于内容的过滤应该利用诸如电影长度或导演等特征，而不是基于其他用户偏好的功能。

Answer 2

我猜你的倾向是正确的，你正在结合内容和协同过滤。如果您使用基于内容，则项目和用户的向量可以称为数据的 x_i（如数据点），而 A_ij 是输入数组中的单元格，说明用户 i 对项目 j 的评分可以称为义。您正在使用 cosine-similarity 来查找 item-item 和 user-user 之间的相似性。我想在您的情况下，您应该进行协作。

尝试制作item-item的矩阵，然后计算余弦相似度。