The relevance feedback information needs to be interpolated with the original query to improve retrieval performance, such as the well-known Rocchio algorithm.
A performance metric which became popular around 2005 to measure the usefulness of a ranking algorithm based on the explicit relevance feedback is normalized discounted cumulative gain.
[1] There are many signals during the search process that one can use for implicit feedback and the types of information to provide in response.
[2][3] The key differences of implicit relevance feedback from that of explicit include:[4] An example of this is dwell time, which is a measure of how long a user spends viewing the page linked to in a search result.
It automates the manual part of relevance feedback, so that the user gets improved retrieval performance without an extended interaction.
[5] Through a query expansion, some relevant documents missed in the initial round can then be retrieved to improve the overall performance.