Social Recommender Systems Tutorial - 2011

【文件属性】：
文件名称：Social Recommender Systems Tutorial - 2011
文件大小：9.97MB
文件格式：RAR
更新时间：2014-09-22 16:49:54
Social Recommender Systems Tutorial Abstract The goal of this tutorial is to expose participants to the current research on social recommender systems (i.e., recommender systems for the social web). Participants will become fabvbamiliar with state-of-the-art recommendation methods, their classifications according to various criteria, common evaluation methodologies, and potential applications that can utilize social recommender systems. Additionally, open issues and challenges in the field will be discussed. Recommendation for the Social Web Social media sites have become tremendously popular in recent years. Prominent examples include photo and video sharing sites such as Flickr and YouTube, blog and wiki systems such as Blogger and Wikipedia, social tagging sites such as Delicious, social network sites such as MySpace and Facebook, and micro-blogging sites such as Twitter. Millions of users are active daily in these sites, creating rich information online that has not been available before. Yet, the abundance and popularity of social media sites floods users with huge volumes of information and hence poses a great challenge in terms of information overload. Social Recommender Systems (SRSs) aim to alleviate information overload over social media users by presenting the most attractive and relevant content. SRSs also aim at increasing adoption, engagement, and participation of new and existing users of social media sites. Recommendations of content (blogs, wikis, etc.) [5], tags [7], people [3], and communities [2] often use personalization techniques adapted to the needs and interests of the individual user, or a set of users [6].