论文标题
基于主题标签分析的朋友建议
Friend Recommendation based on Hashtags Analysis
论文作者
论文摘要
社交网络包括数百万用户不断寻找用于个人或专业目的的新关系。社交网站根据关系特征和内容信息推荐朋友。每天共享的大部分信息都在主题标签中传播。现有的基于内容的推荐系统都没有使用主题标签的语义，同时建议新朋友。目前，主题标签被视为字符串，而无需查看其含义。社交网站将人们分享的人们分享完全相同的主题标签，而从未在语义上结束。我们认为标签封装了某些人的兴趣。在本文中，我们提出了一个框架，以显示推荐系统如何从主题标签中受益，以丰富用户的配置文件。该框架由三个主要组成部分组成：（1）基于共享主题标签构建用户配置文件，（2）匹配方法，该方法计算配置文件之间的语义相似性，（3）使用群集技术对语义上关闭用户进行分组。该框架已从斯坦福大型网络数据集集合中的Twitter数据集上进行了测试，该数据集集合由81306个配置文件组成。
Social networks include millions of users constantly looking for new relationships for personal or professional purposes. Social network sites recommend friends based on relationship features and content information. A significant part of information shared every day is spread in Hashtags. None of the existing content-based recommender systems uses the semantic of hashtags while suggesting new friends. Currently, hashtags are considered as strings without looking at their meanings. Social network sites group together people sharing exactly the same hashtags and never semantically close ones. We think that hashtags encapsulate some people interests. In this paper, we propose a framework showing how a recommender system can benefit from hashtags to enrich users' profiles. This framework consists of three main components: (1) constructing user's profile based on shared hashtags, (2) matching method that computes semantic similarity between profiles, (3) grouping semantically close users using clustering technics. The proposed framework has been tested on a Twitter dataset from the Stanford Large Network Dataset Collection consisting of 81306 profiles.
