Section: Research Program

Social Data Management and Crowdsourcing

The social Web blurs today the distinction between search, recommendation, and advertising (three paradigms for information access that have been so far considered mostly in separation). Our research in this area strives to find better adapted and scalable ways to answer information needs in the social Web, often by techniques at the intersection of databases, information retrieval, and data mining.

In particular, we study models and algorithms for personalized, or social-aware search in social applications. While progress has been made in this area, more remains to be done in order to address users' needs in practice, especially towards richer data models, and improving applicability and result relevance. For instance, when searching for tweets, their geographical location and recency may be as important for relevance as the textual and social aspects.

Furthermore, regarding quality of answers in response to searches, for various reasons (e.g., sparsity or tagging quality), meaningful results may often not be available. One response to this observation could be to turn to the crowd, the very users/publishers of the social media platform, and to turn this crowd into on-demand and query-driven sources of data. We study principled approaches for crowd selection (expert sourcing) and task assignment (data sourcing), in order to better answer ongoing social queries.

Beyond social links that represent just ties, a promising direction we also focus on in user-centric applications is to uncover implicit, potentially richer relationships from user interactions and to exploit them to improve core functionality such as search.

Moreover, we plan to investigate how crowdsourcing can be exploited to extract informations on user preferences, using techniques about noisy data management and provenance analysis.