Search result clustering can also be used to diversify the top results returned by a search engine.
for each query q, one natural way of measuring a system's performance is to calculate the subtopic recall-at-K (Zhai et al., 2003) given by the number of different subtopics retrieved for q in the top K results returned: where subtopics(r i ) is the set of subtopics manually assigned to the search result r i and M is the number of subtopics for query q (note that in our experiments M is the number of subtopics occurring in the 100 results retrieved for q, so S-recall@100 = 1).