Static Index Pruning for Information Retrieval Systems: A Posting-Based Approach

Static index pruning methods have been proposed to reduce size of the inverted index of information retrieval systems. The goal is to increase efficiency (in terms of query response time) while preserving effectiveness (in terms of ranking quality). Current state-of-the-art approaches include the term-centric pruning approach and the document-centric pruning approach. While the term-centric pruning considers each inverted list independently and removes less important postings from each inverted list, the document-centric approach considers each document independently and removes less important terms from each document. In other words, the term-centric approach does not consider the relative importance of a posting in comparison with others in the same document, and the document-centric approach does not consider the relative importance of a posting in comparison with others in the same inverted list. The consequence is less important postings are not pruned in some situations, and important postings are pruned in some other situations. We propose a posting-based pruning approach, which is a generalization of both the term-centric and document-centric approaches. This approach ranks all postings and keeps only a subset of top ranked ones. The rank of a posting depends on several factors, such as its rank in its inverted list, its rank in its document, its weighting score, the term weight and the document weight. The effectiveness of our approach is verified by experiments using TREC queries and TREC datasets.