Yannick Naudet

Personality Matters: Balancing for Personality Types Leads to Better Outcomes for Crowd Teams

When personalities clash, teams operate less effectively. Personality differences affect face-to-face collaboration and may lower trust in virtual teams. For relatively short-lived assignments, like those of online crowdsourcing, personality matching could provide a simple, scalable strategy for effective team formation. However, it is not clear how (or if) personality differences affect teamwork in this novel context where the workforce is more transient and diverse. This study examines how personality compatibility in crowd teams affects performance and individual perceptions. Using the DISC personality test, we composed 14 five-person teams (N=70) with either a harmonious coverage of personalities (balanced) or a surplus of leader-type personalities (imbalanced). Results show that balancing for personality leads to significantly better performance on a collaborative task. Balanced teams exhibited less conflict and their members reported higher levels of satisfaction and acceptance. This work demonstrates a simple personality matching strategy for forming more effective teams in crowdsourcing contexts.

Gaming and Cognitive Profiles for Recommendations in Museums

This paper presents an approach to enhance museum visitors experience through the use of Gaming, Social Networks, and Recommendations. The originality of the dedicated social and mobile visit personalisation system is that it relies on both the users cognitive profile and his interests, inferred from a game on Facebook.

Guided Crowdsourcing for Collective Work Coordination in Corporate Environments

Crowdsourcing is increasingly gaining attention as one of the most promising forms of large-scale dynamic collective work. However current crowdsourcing approaches do not offer guarantees often demanded by consumers, for example regarding minimum quality, maximum cost or job accomplishment time. The problem appears to have a greater impact in corporate environments because in this case the above-mentioned performance guarantees directly affect its viability against competition. Guided crowdsourcing can be an alternative to overcome these issues. Guided crowdsourcing refers to the use of Artificial Intelligence methods to coordinate workers in crowdsourcing settings, in order to ensure collective performance goals such as quality, cost or time. In this paper, we investigate its potential and examine it on an evaluation setting tailored for intra and inter-corporate environments.

Interconnecting Objects, Visitors, Sites and (Hi)Stories Across Cultural and Historical Concepts: The CrossCult Project

Human History, is a huge mesh of interrelated facts and concepts, spanning beyond borders, encompassing global aspects and finally constituting a shared, global experience. This is especially the case regarding European history, which is highly interconnected by nature; however, most History-related experiences that are today offered to the greater public, from schools to museums, are siloed. The CrossCult project aims to provide the means for offering citizens and cultural venue visitors a more holistic view of history, in the light of cross-border interconnections among pieces of cultural heritage, other citizens viewpoints and physical venues. To this end, the CrossCult project will built a comprehensive knowledge base encompassing information and semantic relationships across cultural information elements, and will provide the technological means for delivering the contents of this knowledge base to citizens and venue visitors in a highly personalized manner, creating narratives for the interactive experiences that maximise situational curiosity and serendipitous learning. The CrossCult platform will also exploit the cognitive/emotional profiles of the participants as well as temporal, spatial and miscellaneous features of context, including holidays and anniversaries, social media trending topics and so forth.

Personalisation in Crowd Systems

This paper discusses the use of personalisation in crowd systems. Crowd Systems are systems, which may be virtual or physical, and which involve a crowd of people in the realisation of one or more system-level objective(s). We define Crowd Systems in terms of the General System Theory, propose a classification scheme and discuss their complexity. Then we highlight the benefit of using personalisation in such systems. We introduce the concept of Personalised Crowd System, its specificities and the associated research challenges. The discussion is further illustrated with examples and research results of specific cases. To our knowledge this is the first paper attempting to examine the usage and impact of personalisation on Crowd Systems.

User Profiling: Towards a Facebook Game that Reveals Cognitive Style

This paper presents an innovative approach based on social-network gaming, which will extract players’ cognitive styles for personalization purposes. Cognitive styles describe the way individuals think, perceive and remember information and can be exploited to personalize user interaction. Questionnaires are usually employed to identify cognitive styles, a tedious process for most users. Our approach relies on a Facebook game for discovering potential visitors’ cognitive styles with an ultimate goal of enhancing the overall visitors’ experience in the museum. By hosting such a game on the museum’s webpage and on Facebook, the museum aims to attract new visitors, as well as to support the user profiling process.

Improving Wiki Article Quality Through Crowd Coordination: A Resource Allocation Approach

In this paper the authors propose a crowd coordination mechanism to increase the quality of articles produced in wiki systems. Wikis constitute promising social digital ecosystems for collaborative knowledge creation on the Web. However, as a result of the purely self-coordinated manner that they function, they cannot ensure the quality of the produced articles-an issue that affects their reliability and acceptance. The authors show that wiki article quality optimization can be formulated as a resource allocation problem. Contributors are selected from the wiki community crowd according to their skills, and matched to the articles they can improve the most. A model of the English Wikipedia is given, parameterized and validated from recent field studies results. Experimental results were obtained with simulation systems implementing this model and on a series of scenarios, which include an analysis of the impact of using semantic relations between wiki domains. The obtained results indicate that the proposed mechanism can lead to the production of wiki articles of higher quality, compared to the respective results achieved by the fully self-coordinated wiki.