Using the Crowd to Improve Search Result Ranking and the Search Experience - Kim, Yubin; Collins-Thompson, Kevyn; Teevan, Jaime (ACM Transactions on Intelligent Systems and Technology (TIST), 2010)
Despite technological advances, algorithmic search systems still have difficulty with complex or subtle information needs. For example, scenarios requiring deep semantic interpretation are a challenge for computers. People, on the other hand, are well-suited to solving such problems. As a result, there is an opportunity for humans and computers to collaborate during the course of a search in a way that takes advantage of the unique abilities of each. While search tools that rely on human intervention will never be able to respond as quickly as current search engines do, recent research suggests that there are scenarios where a search engine could take more time if it resulted in a much better experience. This paper explores how crowdsourcing can be used at query time to augment key stages of the search pipeline. We first explore the use of crowdsourcing to improve search result ranking. When the crowd is used to replace or augment traditional retrieval components such as query expansion and relevance scoring, we find that we can increase robustness against failure for query expansion and improve overall precision for results filtering. However, the gains that we observe are limited and unlikely to make up for the extra cost and time that the crowd requires. We then explore ways to incorporate the crowd into the search process that more drastically alter the overall experience. We find that using crowd workers to support rich query understanding and result processing appears to be a more worthwhile way to make use of the crowd during search. Our results confirm that crowdsourcing can positively impact the search experience, but suggest that significant changes to the search process may be required for crowdsourcing to fulfill its potential in search systems.