Relevance ranking for proximity full-text search based on additional indexes with multi-component keys
This addresses the trade-off between speed and quality in search systems for users handling large datasets, but it is incremental as it builds on known ranking methods.
The paper tackles the problem of how multi-component key indexes affect search quality in proximity full-text search, finding that they produce relevance rankings very close to those from ordinary inverted indexes while previously showing up to 130 times speed improvements for queries with high-frequency words.
The problem of proximity full-text search is considered. If a search query contains high-frequently occurring words, then multi-component key indexes deliver an improvement in the search speed compared with ordinary inverted indexes. It was shown that we can increase the search speed by up to 130 times in cases when queries consist of high-frequently occurring words. In this paper, we investigate how the multi-component key index architecture affects the quality of the search. We consider several well-known methods of relevance ranking, where these methods are of different authors. Using these methods, we perform the search in the ordinary inverted index and then in an index enhanced with multi-component key indexes. The results show that with multi-component key indexes we obtain search results that are very close, in terms of relevance ranking, to the search results that are obtained by means of ordinary inverted indexes.