Rewarding Social Recommendation in OSNs: Empirical Evidences, Modeling and Optimization

Abstract

In the past few years, many companies are considering “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">social recommendation</i> ” for their businesses, e.g., firms are offering rewards to customers who recommend the firms’ products/services in online social networks (OSNs). However, the pros and cons of such social recommendation scheme are still unclear. Thus, it is difficult for firms to design rewarding schemes, and for OSN platforms to design regulating policies. By analyzing real data from Weixin and Yelp, we first identify key factors that affect the spreading of products/services in OSNs. These findings enable us to develop an accurate (i.e., with a high validation accuracy) mathematical model on social recommendations. Our model captures how users decide whether to recommend an item, which is a key factor but often ignored by previous social recommendation models such as the “ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Independent Cascade model</i> ”. We also design algorithms to infer model parameters. Using our model, we uncover conditions when social recommendation improves a firm’s profit and users’ utilities, as well as when it cannot improve the profit or hurts users’ utilities. These conditions help the design of both rewarding schemes and regulating policies. Moreover, we extend our model to a dynamic setting, so that a firm can improve its profit by dynamically optimizing its rewarding schemes.