论文标题
通过在线社交网络确定广告活动的$ k $最佳目标
Identifying the $k$ Best Targets for an Advertisement Campaign via Online Social Networks
论文作者
论文摘要
我们根据两个主要方面向广告商（例如品牌）推荐推荐广告客户的建议方法：（i）在线社交网络配置文件和（ii）在线社交网络上的邻里分析之间的比较。根据来自社交媒体的文本内容的单词表示，用户和品牌之间的配置文件匹配是考虑的，并且使用诸如术语频率插入文档频率之类的措施来表征比较中单词的重要性。该方法已依靠大数据技术实施，从而可以对非常大的在线社交网络进行有效分析。实际数据集上的结果表明，配置文件匹配和邻里分析的组合成功地识别了最合适的用户集作为给定广告活动的目标。
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.
