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活动简介
The exponential growth of the Social Web poses both challenges and new opportunities for recommender systems research. The Social Web has turned information consumers into active contributors creating massive amounts of information. Finding relevant and interesting content at the right time and in the right context is challenging for existing recommender approaches. At the same time, social systems by their definition encourage interaction between users and both online content and other users, thus generating new sources of knowledge for recommender systems. Users of social media on the Web often explicitly provide personal information or implicitly express preferences through their interactions with other users or with resources (e.g. tagging, friending, rating, commenting, etc.). This Social Web therefore provides huge opportunities for recommender technology and in turn recommender technologies can play a part in fuelling the success of the Social Web phenomenon.
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重要日期

2014-07-27
初稿截稿日期

征稿范围

The goal of this workshop is to bring together researcher and practitioners to explore, discuss, and understand challenges and new opportunities for recommender systems and the Social Web. We solicit original contributions in the following areas: Social network and folksonomy development: Recommending friends, tags, bookmarks, blogs, music, communities etc. Leveraging Models user behavior on the Social Web for recommendation Recommender systems mash-ups, intelligent user interfaces, rich media recommender systems Collaborative knowledge authoring, collective intelligence Topic emergence and evolution on the Social Web and their role in recommendation process Recommender applications involving users or groups directly in the recommendation process Exploiting folksonomies, social network information, user interactions, and communities recommendation process The role of context in Social Web recommendation Trust and reputation aware social recommendations Semantic Web recommender systems, use of ontologies or microformats Empirical evaluation of social recommender techniques, success and failure measures Case studies and novel fielded social recommender applications Economy of community-based systems: Using recommenders to encourage users to contribute and sustain participation Social recommender systems in the enterprise Recommendation for groups Processing big Social Web data volumes for recommendation, scalability issues
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重要日期
  • 10月06日

    2014

    会议日期

  • 07月27日 2014

    初稿截稿日期

  • 10月06日 2014

    注册截止日期

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美国计算机学会
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