A Model-Driven Approach for Crowdsourcing Search

Even though search systems are very ecient in retrieving world-wide information, they can not capture some peculiar aspects and features of user needs, such as subjective opin- ions and recommendations, or information that require local or domain specic expertise. In this kind of scenario, the hu- man opinion provided by an expert or knowledgeable user can be more useful than any factual information retrieved by a search engine. In this paper we propose a model-driven approach for the specication of crowd-search tasks, i.e. activities where real people { in real time { take part to the generalized search process that involve search engines. In particular we dene two models: the\Query TaskModel", representing the meta- model of the query that is submitted to the crowd and the associated answers; and the \User Interaction Model", which shows how the user can interact with the query model to fulll her needs. Our solution allows for a top-down design approach, from the crowd-search task design, down to the crowd answering system design. Our approach also grants automatic code generation thus leading to quick prototyping of search applications based on human responses collected over social networking or crowdsourcing platforms.