Multi-Perspective Recommender Systems

Recommender systems play a critical role in online business as relevant personalized recommendations can have huge impact on users' consumption and purchase decisions. With deeper involvement of such automatic decision support in various specific contexts and tasks, concerns also arise regarding appropriate application and adaptation of the systems to generate high-quality recommendations. This stream of research aims at addressing some of the existing challenges in this field by providing multi-perspective solutions.

Complementing Individual Predictions for Intelligent Decision Support

Applying data-driven decision support in real-world contexts is complex in that the automated predictions are often imperfect due to noisy, limited data or simplified mathematical or statistical assumptions. The imperfection of predictive models poses challenges in decision-making facilitation, especially in highly risk-sensitive domains like pharmaceutical research, medical diagnosis, or financial markets. This stream of research advocates multi-dimensional evaluation of data-driven predictive modeling solutions for the purpose of their intelligent applications and make methodological contributions to advance predictive-analytics-based decision support.

Retailer Channel Management in the Digital Age

The rapid development of the Internet technologies and their applications to the retailing industry has made the online channel an imperative consumer touchpoint. In practice, retailers have experimented with multi-channel strategies by having online storefronts to supplement their existing pool of offline stores. This set of works combine different research methods to study the economic value stemming from the synergy between online and offline channels, understand whetehr and how different channels would improve overall business performance and adapt data analytics to avoid malicious product-related information manipulation in the online channel.