使用社交媒体上粉丝的帖子来衡量品牌之间的相似性(Multimedia)

• 2020 年 1 月 13 日
• 筆記

在这篇论文中，我们提出了一种新的测量方法，通过社交网络服务(SNS)上的品牌追随者的帖子来估计品牌之间的相似性。我们的方法是为了探索客户可能共同购买的品牌而开发的。如今，品牌使用社交媒体进行定向广告，因为影响用户的喜好可以极大地影响销售趋势。我们假设SNS的数据可以让我们对品牌进行定量比较。我们提出的算法分析了每个品牌的追随者每天发布的照片和标签。通过将它们聚类并转换成直方图，我们可以计算出品牌之间的相似性。我们通过购买记录、信用卡信息和问卷回答来评估我们提出的算法。实验结果表明，由商场或信用卡公司维护的购买数据可以很好地预测消费者的共同购买行为，但不能很好地预测消费者购买新品牌产品的意愿。另一方面，我们的方法可以预测用户对品牌的兴趣，相关值在0.53以上，考虑到用户对品牌的兴趣具有较高的主观性和个体依附性，这是相当高的。

原文标题：Multimedia: Measuring Similarity between Brands using Followers' Post in Social Media

原文：In this paper, we propose a new measure to estimate the similarity between brands via posts of brands' followers on social network services (SNS). Our method was developed with the intention of exploring the brands that customers are likely to jointly purchase. Nowadays, brands use social media for targeted advertising because influencing users' preferences can greatly affect the trends in sales. We assume that data on SNS allows us to make quantitative comparisons between brands. Our proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them to histograms, we can calculate the similarity between brands. We evaluated our proposed algorithm with purchase logs, credit card information, and answers to the questionnaires. The experimental results show that the purchase data maintained by a mall or a credit card company can predict the co-purchase very well, but not the customer's willingness to buy products of new brands. On the other hand, our method can predict the users' interest on brands with a correlation value over 0.53, which is pretty high considering that such interest to brands are high subjective and individual dependent.

原文作者：Yiwei Zhang,Xueting Wang,Yoshiaki Sakai,Toshihiko Yamasaki 原文链接：https://arxiv.org/abs/2001.03353