我们提出了一种新颖的方法，用于向广告商（如品牌）推荐可能的客户（用户），主要基于两个方面。(1)在线社交网络的资料比较，和(2)在线社交网络的邻域分析。用户和品牌之间的档案匹配是基于来自社交媒体的文本内容的词袋展示，并使用术语频率反向文档频率等措施来描述词在比较中的重要性。该方法是依靠大数据技术实现的，这样就可以对规模大的在线社交网络进行有效分析。在真实数据集上的结果表明，配置文件匹配和邻域分析的组合成功地识别出最合适的用户集，作为一个给定的广告活动的目标。

原文题目：Identifying the k Best Targets for an Advertisement Campaign via Online Social Networks

原文：We propose a novel approach for the recommendation of possible customers (users) to advertisers (e.g., brands) based on two main aspects: (i) the comparison between On-line Social Network profiles, and (ii) neighborhood analysis on the On-line Social Network. Profile matching between users and brands is considered based on bag-of-words representation of textual contents coming from the social media, and measures such as the Term Frequency-Inverse Document Frequency are used in order to characterize the importance of words in the comparison. The approach has been implemented relying on Big Data Technologies, allowing this way the efficient analysis of very large Online Social Networks. Results on real datasets show that the combination of profile matching and neighborhood analysis is successful in identifying the most suitable set of users to be used as target for a given advertisement campaign.

原文作者：Mariella Bonomo, Armando La Placa, Simona E. Rombo

原文地址：https://arxiv.org/abs/2008.02108