Complementary to the fancy applications of social networks, influence analysis is an
indispensable technique supporting these practical applications. In recent years, this …

The ongoing rapid expansion of the Internet greatly increases the necessity of effective
recommender systems for filtering the abundant information. Extensive research for …

This research develops a conceptualized model that illuminates the role of luxury
perceptions in explaining consumer engagement in social media WOM and luxury purchase …

In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …

Data sparsity is one of the most challenging problems for recommender systems. One
promising solution to this problem is cross-domain recommendation, ie, leveraging …

Research on social networks has exploded over the last decade. To a large extent, this has
been fueled by the spectacular growth of social media and online social networking sites …

Social influence analysis has become one of the most important technologies in modern
information and service industries. Thus, how to measure social influence of one user on …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

Influence maximization, defined as a problem of finding a set of seed nodes to trigger a
maximized spread of influence, is crucial to viral marketing on social networks. For practical …

Social networks are commonly used for marketing purposes. For example, free samples of a
product can be given to a few influential social network users (or seed nodes), with the hope …