Topic-model based Estimation of Passive Twitter-User's Interests from Followed Users' Tweets

Tessai Hayama, Qi Zhang
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引用次数: 1

Abstract

User modeling based on the contents of social network services has been developed to recommend information related to the preference of each user. Most of the previous studies have analyzed active users' tweets and estimated their interests. Meanwhile, although there are more than a certain number of passive users, who do not tweet but only gather information, little research has been conducted on interest estimation for them due to the lack of clues for estimating their interests. In this study, we developed an interest estimation method for passive Twitter users from the tweets of followed users by applying an interest topic extraction method for active users. In our evaluation, we confirmed the effectiveness of the proposed method by comparison with simple topic extraction methods based on data with interest topic evaluation of 12 users.
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基于主题模型的被动推特用户兴趣估计
已经开发了基于社交网络服务内容的用户建模,以推荐与每个用户的偏好相关的信息。之前的大多数研究都是分析活跃用户的推文并估计他们的兴趣。与此同时,虽然有一定数量以上的被动用户,他们不发推,只收集信息,但由于缺乏对他们兴趣的估计线索,对他们的兴趣估计研究很少。在本研究中,我们采用针对活跃用户的兴趣话题提取方法,从关注用户的推文中开发了一种针对被动Twitter用户的兴趣估计方法。在我们的评估中,我们基于12个用户的兴趣话题评价数据,通过与简单的话题提取方法进行对比,证实了本文方法的有效性。
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