Adapting Rankers Online

摘要

At the heart of many effective approaches to the core information retrieval problem— identifying relevant content—lies the following three-fold strategy: obtaining content-based matches, inferring additional ranking criteria and constraints, and combining all of the above so as to arrive at a single ranking of retrieval units. Over the years, many models have been proposed for content-based matching, with particular attention being paid to estimations of query models and document models. Different task and user scenarios have given rise to the study and use of priors and non-content-based ranking criteria such as freshness, authoritativeness, and credibility. The issue of search result combinations, whether ranked-based, score-based or both, has been a recurring theme for many years.