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MDER: Multi-Dimensional Event Recommendation in Social Media Context
The Computer Journal ( IF 1.5 ) Pub Date : 2020-10-22 , DOI: 10.1093/comjnl/bxaa126
Abir Troudi 1 , Leila Ghorbel 1 , Corinne Amel Zayani 1 , Salma Jamoussi 2 , Ikram Amous 1
Affiliation  

Events represent a tipping point that affects users’ opinions and vary depending upon their popularity from local to international. Indeed, social media offer users platforms to express their opinions and commitments to events that attract them. However, owing to the volume of data, users are encountering a difficulty to accede to the preferred events according to their features that are stored in their social network profiles. To surmount this limitation, multiple event recommendation systems appeared. Nevertheless, these systems use a limited number of event dimensions and user’s features. Besides, they consider users’ features stored in a single user’s profile and disregard the semantic concept. In this research, an approach for multi-dimensional event recommendation is set forward to recommend events to users resting on several event dimensions (engagement, location, topic, time and popularity) and some user’s features (demographic data, position and user’s/friend’s interests) stored in multi-user’s profiles by considering the semantic relationships between user’s features, specifically user’s interests. The performance of our approach was assessed using error rate measurements (mean absolute error, root mean squared error and cross-validation). Experiment that results on real-world event data sets confirmed that our approach recommends events that fit the user more than the previous approaches with the lowest error rate values.

中文翻译:

MDER:社交媒体环境中的多维事件推荐

事件代表了一个影响用户意见的转折点,并且取决于他们从本地到国际的受欢迎程度。确实,社交媒体为用户提供了表达他们对吸引他们的事件的观点和承诺的平台。但是,由于数据量大,用户遇到了根据存储在其社交网络配置文件中的特征来加入首选事件的困难。为了克服此限制,出现了多个事件推荐系统。然而,这些系统使用有限数量的事件维度和用户特征。此外,他们考虑存储在单个用户个人资料中的用户特征,而忽略了语义概念。在这项研究中 提出了一种多维事件推荐的方法,可以将事件推荐给基于多个事件维度(参与度,位置,主题,时间和受欢迎程度)以及存储在多个事件维度中的某些用户特征(人口统计数据,位置和用户/朋友的兴趣)的用户-通过考虑用户功能(特别是用户兴趣)之间的语义关系来确定用户的个人资料。我们使用错误率测量(平均绝对误差,均方根误差和交叉验证)评估了该方法的性能。对真实事件数据集进行的实验证实,与以前的方法相比,我们的方法建议的用户更适合用户,这些方法的错误率值最低。位置和用户/朋友的兴趣)存储在多用户个人资料中,方法是考虑用户特征之间的语义关系,尤其是用户的兴趣。我们使用错误率测量(平均绝对误差,均方根误差和交叉验证)评估了该方法的性能。对真实事件数据集进行的实验证实,与以前的方法相比,我们的方法建议的用户更适合用户,这些方法的错误率值最低。位置和用户/朋友的兴趣)存储在多用户个人资料中,方法是考虑用户特征之间的语义关系,尤其是用户的兴趣。我们使用错误率测量(平均绝对误差,均方根误差和交叉验证)评估了该方法的性能。对真实事件数据集进行的实验证实,与以前的方法相比,我们的方法建议的用户更适合用户,这些方法的错误率值最低。
更新日期:2020-10-22
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