Fast candidate generation for two-phase document ranking

Most modern web search engines employ a two-phase ranking strategy: a candidate list of documents is generated using a "cheap" but low-quality scoring function, which is then reranked by an "expensive" but high-quality method (usually machine-learned). This paper focuses on the problem of candidate generation for conjunctive query processing in this context. We describe and evaluate a fast, approximate postings list intersection algorithms based on Bloom filters. Due to the power of modern learning-to-rank techniques and emphasis on early precision, significant speedups can be achieved without loss of end-to-end retrieval effectiveness. Explorations reveal a rich design space where effectiveness and efficiency can be balanced in response to specific hardware configurations and application scenarios.