【24h】

The influence limiter

机译:影响限制器

获取原文

摘要

Recommender systems have emerged as an important part of the solution to the information overload problem facing today's Web users. Combining ideas and techniques from information filtering, user modeling, artificial intelligence, user interface design and human-computer interaction, recommender systems provide users with proactive suggestions that are tailored to meet their particular information needs and preferences. Indeed recommender systems have enjoyed considerable commercial success and continue to play an increasingly important role in many online services, from Amazon and iTunes to Tivo and Last.fm. >It is our great pleasure to welcome you to the First ACM Recommender Systems conference, which builds on a wonderful legacy of research workshops and the Recommenders06 Summer School in Bilbao on the Present and Future of Recommender Systems. This conference brings together leaders in the field, from both research and practice, to explore the latest innovations, challenges, andopportunities for recommender systems technologies. >The call for papers attracted 35 long paper submissions and 23 short paper submissions from 15 countries. After a detailed scientific review process the program committee selected 16 full-length papers (a 46% acceptance rate) and 14 short-papers (a 61% acceptance rate) for publication in the proceedings and presentation at the conference. >In addition, the conference program includes a keynote presentation by Google's Krishna Bharat, the creator of Google News; two panel sessions filled with industry leaders sharing experiences and raising challenges for the research community; and industry-track submissions intended to provoke discussion. The conference also includes a doctoral symposium to provide an opportunity for doctoral students to explore and develop their research interests in an interdisciplinary workshop, under the guidance of a panel of distinguished research faculty.
机译:推荐系统已成为解决当今Web用户面临的信息过载问题的解决方案的重要组成部分。结合信息过滤,用户建模,人工智能,用户界面设计和人机交互,推荐系统的思路和技术为用户提供了符合其特定信息需求和偏好的主动建议。确实推荐系统享有相当大的商业成功,并继续在许多在线服务中发挥越来越重要的作用,从亚马逊和itunes到Tivo和Last.fm。 >我们很高兴欢迎您来到第一个ACM推荐制度会议,建立在毕尔巴鄂的一系列研究工作室和建议人员06暑期学校的当前和推荐系统的未来。本次会议在研究和实践中汇集了该领域的领导者,探讨了推荐系统技术的最新创新,挑战,且opopportonities。 >论文的呼吁吸引了35个长的纸张提交和23份短文提交来自15个国家。在详细的科学审查过程之后,计划委员会选出了16篇全长论文(A 46%的验收率)和14个短篇论文(61%的接受费率),在会议上的诉讼程序中出版和发表。 <此外,会议计划还包括谷歌新闻创造者的谷歌克里希纳Bharat的主题演讲;两个小组会议,充满了行业领导者分享经验并提高研究界的挑战;和行业轨道提交意图挑起讨论。会议还包括博士专题讨论会,为博士生探讨和发展他们在跨学科研讨会的研究兴趣,并在杰出的研究学院的指导下进行跨学科研讨会。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号