A Data Mining Approach for Developing Online Streaming Recommendations

Abstract

Online streaming has become increasingly popular with the availability of broadband networks and the increase in computing power and electronic distribution. Online streaming operators have difficulty developing flexible business alternatives according to users’ changing streaming behaviors in terms of generating a good and profitable business model. In terms of e-commerce development, the live-streaming platform that provides streaming of the main merchandise to users, allowing the users to directly consume via live-streaming become critical issues. In this regard, personalized recommendation systems can use the user’s interests and purchasing behavior to recommend information and merchandise. Thus, this study investigates the online streaming experiences of Taiwanese consumers to evaluate online streaming users and their online purchase behaviors for developing online recommendations. This study uses a snowflake schema, which is an extension of the star schema. In addition, this study develops a rule-based recommendation approach for investigating online streaming and purchasing behaviors in terms of online recommendations. This study is the first to determine how online streaming proprietors and their affiliates are disseminated using online streaming consumer behaviors in terms of online recommendations for further electronic commerce development.