Declarative user selection with soft constraints

Yael Amsterdamer, Amit Somech, Tova Milo, Brit Youngmann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In applications with large userbases such as crowdsourcing, social networks or recommender systems, selecting users is a common and challenging task. Different applications require different policies for selecting users, and implementing such policies is application-specific and laborious. To this end, we introduce a novel declarative framework that abstracts common components of the user selection problem, while allowing for domain-specific tuning. The framework is based on an ontology view of user profiles, with respect to which we define a query language for policy specification. Our language extends SPARQL with means for capturing soft constraints which are essential for worker selection. At the core of our query engine is then a novel efficient algorithm for handling these constraints. Our experimental study on real-life data indicates the effectiveness and flexibility of our approach, showing in particular that it outperforms existing task-specific solutions in prominent user selection tasks.

Original languageEnglish
Title of host publicationCIKM 2019 - Proceedings of the 28th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450369763
StatePublished - 3 Nov 2019
Event28th ACM International Conference on Information and Knowledge Management, CIKM 2019 - Beijing, China
Duration: 3 Nov 20197 Nov 2019

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference28th ACM International Conference on Information and Knowledge Management, CIKM 2019


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