论文标题
使用社交媒体中的追随者帖子来衡量品牌之间的相似性
Measuring Similarity between Brands using Followers' Post in Social Media
论文作者
论文摘要
在本文中，我们提出了一项新措施，以通过社交网络服务（SNS）的品牌追随者的帖子来估算品牌之间的相似性。我们的方法是为了探索客户可能共同购买的品牌。如今，品牌使用社交媒体进行有针对性的广告，因为影响用户的喜好会极大地影响销售趋势。我们假设SNS上的数据使我们能够在品牌之间进行定量比较。我们提出的算法分析了每个品牌追随者发布的每日照片和标签。通过将它们聚集并将其转换为直方图，我们可以计算品牌之间的相似性。我们通过购买日志，信用卡信息和问卷的答案评估了提议的算法。实验结果表明，购物中心或信用卡公司维护的采购数据可以很好地预测共购买，但不愿意购买新品牌产品。另一方面，我们的方法可以预测用户对相关价值超过0.53的品牌的兴趣，考虑到品牌的这种兴趣很高，主观和个人依赖。
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.
