Machine Learning with Spark - Second Edition

Content-based filtering

Content-based methods try to use the content or attributes of an item, together with some notion of similarity between two pieces of content, to generate items similar to a given item. These attributes are often textual content, such as titles, names, tags, and other metadata attached to an item, or in the case of media, they could include other features of the item, such as attributes extracted from audio and video content.

In a similar manner, user recommendations can be generated based on attributes of users or user profiles, which are then matched to item attributes using the same measure of similarity. For example, a user can be represented by the combined attributes of the items they have interacted with. This ...