Inductive matrix completion and graph proximity for content item recommendation

Abstract

Users may consume and/or share information through various types of content items. For example, user may post a family photo through a social network, create a running blog through a microblogging service, etc. Because users may be overwhelmed by the amount of available content items, it may be advantageous to recommend content items, such as blogs to follow, to users. Accordingly, inductive matrix completion is used to evaluate user interactions with content items (e.g., a user following a blog), content item features (e.g., text and/or images of a blog is evaluated to identify a topic of the blog), and/or user features (e.g., a user liking or reblogging a blog, user demographics, user interests, etc.) to determine whether to recommend a content item to a user. Additionally, graph proximity is used to recommend content items based upon weights of edges connecting user nodes to content item nodes within a directed graph.