Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

Sevli Onur; Kü?üksille Ecir U?ur

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Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

机译：基于动态数据分析的土耳其语Twitter用户广告推荐系统

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Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user’s profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback.

机译：在线环境，尤其是社交网络已成为广告发布的绝佳替代方案。为了完成有效的广告宣传，重要的是内容必须符合目标受众的期望。考虑到期望可能随时间改变，因此需要实时动态地识别用户的方向。在这项研究中，实时分析了土耳其Twitter用户共享的消息，并确定了用户的即时期望。为了完成这项工作，设计了一个网络服务，该服务可以分析用户的个人资料并展示最适合期望的广告。为了过滤出最合适的广告内容，一种称为启发式修剪方法（HPM）的方法已经被揭示出来。已开发的系统已经在自愿使用Twitter的自愿参加者小组上进行了测试，收到的反馈证明了该系统的有效性。

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《Technical Gazette》 |2017年第2期|共8页
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Sevli Onur; Kü?üksille Ecir U?ur;
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Advertising recommendation system based on dynamic data analysis on Turkish speaking Twitter users

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