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"Un forum autour des RH 2.0 soulevait récemment la question de « pourquoi laisser les employés auto-déclarer leur compétences dans le réseau social de l'entreprise? ». Cette question fait partie des quelques unes au cœur de la démarche d’entreprise 2.0 qui soulèvent de nombreuses interrogations. Elle traduit très concrètement la philosophie même du 2.0 et révèle aussi certaines craintes qui lui sont rattachées"

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"In John Hopson’s article Behavioral Game Design he shares the basic ways people react to different patterns of rewards. He ends the article with this: “Each contingency is an arrangement of time, activity, and reward, and there are an infinite number of ways these elements can be combined to produce the pattern of activity you want from your players.”"

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"Over the past few years, as organizations deploy social tools, expertise location has become one of the more common solutions associated with "Enterprise 2.0". The general assumption includes two primary ways of identifying "experts". The first method assumes that employee use of social tools (e.g., blogs, wikis, micro-blogging, communities) and social applications (e.g., ideation), enables their talent and business insight to be more visible and therefore more discoverable by co-workers. The second method revolves around the employee profile created as part of an enterprise social network site. It is assumed that employees will readily create and maintain rich profiles where they willingly share information about their job history, interests, hobbies, education, and areas of expertise."

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SRI International based in Menlo Park, California, teamed up with military officers to build a new social analytics tool called iLink that generates models and helps streamline the process by which a specific expert in an online community can be found.

In simple terms, iLink is a machine learning-based system that models users and content in a social network and then points the user to relevant content.

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The iLink system had several goals, including real-time learning by matching queries and communities users; adapting to user demands and directions, providing accuracy in message targeting and routing and, finally, dynamic user profile correction based on community behaviours and identification of community experts.

The learning in iLink occurs by watching a natural social network, and selecting effective strategies that emerge from the system as the members try to solve problems. The system continuously monitors the real social network and it is capable of drafting from the social network's learning.

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The iLink software uses artificial intelligence software and message routing technology to help the system learn about the online participants and move specific questions to those who are best equipped to answer them. The SRI scientists basically build a profile of each person in the community and the iLink system starts to learn about the movement of information around the community.

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