Recommender Systems Research: A Connection-Centric Survey
Journal of Intelligent Information Systems
Computing and applying trust in web-based social networks
Computing and applying trust in web-based social networks
Peekaboom: a game for locating objects in images
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Designing games with a purpose
Communications of the ACM - Designing games with a purpose
Voting techniques for expert search
Knowledge and Information Systems
Mechanisms for making crowds truthful
Journal of Artificial Intelligence Research
Ad-hoc object retrieval in the web of data
Proceedings of the 19th international conference on World wide web
Word sense disambiguation via human computation
Proceedings of the ACM SIGKDD Workshop on Human Computation
Design and implementation of relevance assessments using crowdsourcing
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
In search of quality in crowdsourcing for search engine evaluation
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Crowdsourcing for book search evaluation: impact of hit design on comparative system ranking
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Collective entity linking in web text: a graph-based method
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Repeatable and reliable search system evaluation using crowdsourcing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Pushing the boundaries of crowd-enabled databases with query-driven schema expansion
Proceedings of the VLDB Endowment
Proceedings of the 21st international conference on World Wide Web
Answering search queries with CrowdSearcher
Proceedings of the 21st international conference on World Wide Web
Foundations and Trends in Information Retrieval
Combining inverted indices and structured search for ad-hoc object retrieval
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
CrowdMap: crowdsourcing ontology alignment with microtasks
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part I
Extending search to crowds: a model-driven approach
Search Computing
Large-scale linked data integration using probabilistic reasoning and crowdsourcing
The VLDB Journal — The International Journal on Very Large Data Bases
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Crowdsourcing allows to build hybrid online platforms that combine scalable information systems with the power of human intelligence to complete tasks that are difficult to tackle for current algorithms. Examples include hybrid database systems that use the crowd to fill missing values or to sort items according to subjective dimensions such as picture attractiveness. Current approaches to Crowdsourcing adopt a pull methodology where tasks are published on specialized Web platforms where workers can pick their preferred tasks on a first-come-first-served basis. While this approach has many advantages, such as simplicity and short completion times, it does not guarantee that the task is performed by the most suitable worker. In this paper, we propose and extensively evaluate a different Crowdsourcing approach based on a push methodology. Our proposed system carefully selects which workers should perform a given task based on worker profiles extracted from social networks. Workers and tasks are automatically matched using an underlying categorization structure that exploits entities extracted from the task descriptions on one hand, and categories liked by the user on social platforms on the other hand. We experimentally evaluate our approach on tasks of varying complexity and show that our push methodology consistently yield better results than usual pull strategies.