Improving Search Quality with Automatic Ranking Evaluation and Tuning - Cavalcante, Larícia; Lima, Ullayne; Barbosa, Luciano; Gomes, Ana Luiza; Santana, Éden; Martins, Thiago (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2020: Anais do XXXV Simpósio Brasileiro de Bancos de DadosDO - 10.5753/sbbd.2020.13634, 2020)
Search is a common feature available in document-based applications. It allows users to find information of interest easier. Two essential aspects for building an effective search is to evaluate the ranking quality and be able to efficiently tune it based on this evaluation. In this paper, we present our Automatic Ranking Tuning and Evaluation System (ARTES) that measures the ranking performance based on users’ clicks on search resulting pages and automatically tunes the search ranking function by applying a Bayesian Optimization algorithm. Our system is integrated with Elasticsearch, a widely-used search engine, which provides the search functionality. The whole solution is currently used by our customer support platform to help users effectively find relevant information, as our experimental evaluation confirms.