Recommendations are now unavoidable if you work for an e-commerce website. But e-commerce is not the only use case for recommendations. You can also receive recommendations for people you may want to follow on Twitter, meetups you may attend, or repositories you might like knowing about. Knowledge graphs are a good approach to generate those recommendations.
In this section, we are going to use our GitHub graph to recommend to users new repositories they are likely to contribute to or follow. We will explore several possibilities, split into two cases: either your graph contains some social information (users can like or follow each other) or it doesn't. We'll start from the case where you do not have access to any social data since it is the most common one.
Product similarity recommendations
Recommending products, whether we are talking about movies, gardening tools, or meetups, share some common patterns. Here are some common-sense assertions that can...