Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web

B. Mobasher, D. Jannach, Werner Geyer, A. Hotho
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引用次数: 3

Abstract

The new opportunities for applying recommendation techniques within Social Web platforms and applications as well as the various new sources of information which have become available in the Web 2.0 and can be incorporated in future recommender applications are a strong driving factor in current recommender system research for various reasons: (1) 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. Web 2.0 users explicitly provide personal information and implicitly express preferences through their interactions with others and the system (e.g. commenting, friending, rating, etc.). These various new sources of knowledge can be leveraged to improve recommendation techniques and develop new strategies which focus on social recommendation. (2) New application areas for recommender systems emerge with the popularity of the Social Web. Recommenders cannot only be used to sort and filter Web 2.0 and social network information, they can also support users in the information sharing process, e.g., by recommending suitable tags during folksonomy development. (3) Recommender technology can assist Social Web systems through increasing adoption and participation and sustaining membership. Through targeted and timely intervention which stimulates traffic and interaction, recommender technology can play its role in sustaining the success of the Social Web. (4) The Social Web also presents new challenges for recommender systems, such as the complicated nature of human-to-human interaction which comes into play when recommending people and can require more interactive and richer recommender systems user interfaces. The technical papers appearing in these proceedings aim to explore and understand challenges and new opportunities for recommender systems in the Social Web and were selected in a formal review process by an international program committee. Overall, we received 13 paper submissions from 12 different countries, out of which 7 long papers and 1 short paper were selected for presentation and inclusion in the proceedings. The submitted papers addressed a variety of topics related to Social Web recommender systems from the use of microblogging data for personalization over new tag recommendation approaches to social media-based personalization of news.
第四届ACM RecSys推荐系统和社交网络研讨会论文集
在社交网络平台和应用程序中应用推荐技术的新机会,以及在Web 2.0中可以获得的各种新信息来源,可以纳入未来的推荐应用程序,是当前推荐系统研究的强大驱动因素,原因有很多:(1)社会系统的定义鼓励用户与在线内容和其他用户之间的互动,从而为推荐系统产生新的知识来源。Web 2.0用户明确地提供个人信息,并通过与他人和系统的交互(例如评论、加好友、评级等)隐含地表达偏好。可以利用这些不同的新知识来源来改进推荐技术并开发专注于社交推荐的新策略。(2)随着社交网络的普及,推荐系统出现了新的应用领域。推荐器不仅用于对Web 2.0和社交网络信息进行分类和过滤,还可以在信息共享过程中为用户提供支持,例如在folksonomy开发过程中推荐合适的标签。(3)推荐技术可以通过增加采用、参与和维持会员资格来辅助社会网络系统。通过有针对性和及时的干预,刺激流量和互动,推荐技术可以在维持社交网络的成功中发挥作用。(4)社交网络也给推荐系统带来了新的挑战,例如在推荐人时人与人之间交互的复杂性,这需要更多的交互性和更丰富的推荐系统用户界面。这些技术论文旨在探索和理解社交网络推荐系统的挑战和新机遇,并由一个国际项目委员会在正式审查过程中选出。我们总共收到了来自12个不同国家的13篇论文,其中7篇长论文和1篇短论文被选入会议论文集。提交的论文讨论了与社交网络推荐系统相关的各种主题,从使用微博数据进行个性化的新标签推荐方法到基于社交媒体的新闻个性化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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