Rather than having humans manually boost and block individual items, a machine learning search engine can inspect user behavior to put the most relevant item near the top. When user behavior isn’t enough, the machine can be taught to recognize patterns in results and automatically boost documents that are better matches.
Collaborative filtering uses what users actually click on in response to a query and can boost or recommend items that are frequently clicked.