Analyze users' online shopping behavior using interconnected online interest-product network

In recent years, with the rapid development of Internet, more and more users shop and socialize online, which produces a large amount of traffic data. Analyzing users' online shopping behavior is important for merchants to improve profit. Social networks are widely used in studying the relationship among users. In this paper, we apply the concept of social networks to online shopping behavior, and present a novel network perspective on the interconnected nature of online interest and product, allowing us to capture the attribute of online products reflecting users' sociability. Sociability in this paper is not a traditional social connection but common interests(defined by produce visiting behavior) among users. First, we build an interconnected online interest-product network which includes online shopping based social network (OSSN), online product network and intermediate layer, and we define popular online products and non-popular online products according to the number of distinct visitors of one product. Then, we analyze some indicators about OSSN, and find that OSSN has similar characteristics with traditional social networks, such as small world feature and homophily. Finally, we analyze how online products reflect users' sociability. With the number of online products users have visited increasing, the number of neighbors with similar interests first presents a positive correlation, and then disappears, which implies that the number of users' neighbors depends on the attribute of online products rather than the number of online products users have browsed. We define a formula to measure the attribute of online products reflecting users' sociability, and find that there is a distinct difference among different categories of online products, and non-popular online products often have high value of attribute to reflect users' sociability and the value is low in popular online products.