That's exactly correct. Furthermore, the "implicit" part seeks to achieve this without directly asking the user questions regarding his or her intent, but rather by solely regarding user behaviour. For example, if every time you order a slice of pizza it is vegetarian, I might implicitly assume that your favourite type of pizza is vegetarian. Should you ever ask me to order a slice of pizza for you, without specifying what type, I might therefore deliver vegetarian.

"Joachims et al used eye tracking studies combined with manual relevance judgements to investigate the accuracy of clickthrough data for implicit relevance feedback [4]. They conclude that clickthrough data can be used to accurately determine relative document relevancies."

Yes, Professor Joachims is a much respected and widely cited expert in the field of information retrieval. Here is what he says in his paper about the differences between implicit and explicit feedback:

"In contrast to explicit feedback, such implicit feedback has the advantage that it can be collected at much lower cost, in much larger quantities, and without burden on the user of the retrieval system. However, implicit feedback is more difficult to interpret and potentially noisy."

Part of the noise comes from "position bias" or "trust bias," which means that, irrespective of the quality of the results, people are generally more inclined to click results that are more highly ranked. The logarithmic graph in Figure 4 of our research paper has a pretty clear depiction of this. This can be a very large problem when it comes to determining the relative relevancy of search results; however, there are techniques to compensate for this, such as swapping the order of results and comparing relative click-through rates (CTRs).