About the Importance of Interface Complexity and Entropy for Online Information Sharing

In this paper, we describe two experiments that show the powerful influence of interface complexity and entropy on online information sharing behaviour. 134 participants were asked to do a creativity test and answer six open questions against three different screen backgrounds of increasing complexity. Our data shows that, as an interface becomes more complex and has more entropy users refer less to themselves and show less information sharing breadth. However, their verbal creativity and information sharing depth do not suffer in the same way. Instead, an inverse U shaped relationship between Interface complexity and creativity as well as information sharing depth can be observed: Users become more creative and thoughtful until a certain tipping point of interface complexity is reached. At that point, creativity and th inking suffer, leading to significantly less disclosure. This result challenges the general HCI assumption that simplicity is always best for computers interface design , as users'creativity and information sharing depth initially increases with more interface complexity. Our results suggest that the Yerkes Dodson Law may be a key theory underlying online creativity and depth of online disclosures

THE IMPACT OF PRODUCT PHOTO ON ONLINE CONSUMER PURCHASE INTENTION: AN IMAGE-PROCESSING ENABLED EMPIRICAL STUDY

Determinants of online consumer’s purchase decisions are of long-term interest to researchers and practitioners. Since product photos directly aid consumers’ understanding of products, retailers often put a lot of effort into polishing them. However, there is limited research on the impact of product photos on purchase decisions. Most previous studies took an experiment-based approach, which delivered strict theories on some aspects of product photos. This research takes advantage of image-processing techniques to study product photos’ impact. These techniques allow us to investigate a large set of photo characteristics simultaneously in an empirical study. To rule out possible confounding factors, we collect a dataset from a social shopping Website, which has a simple interface allowing users to judge products mainly based on their photos. We examine product photo characteristics from the aspects of information, emotion, aesthetics, and social presence. We found that consumers prefer product photos with a larger key object, lower entropy on key objects, a warmer color, a higher contrast, a higher depth-of-field, and more social presences. This research introduces a Big Data-based approach to study the impact of e-commerce systems’ visual features on consumers

Scalable Exact Parent Sets Identification in Bayesian Networks Learning with Apache Spark

In Machine Learning, the parent set identification problem is to find a set of random variables that best explain selected variable given the data and some predefined scoring function. This problem is a critical component to structure learning of Bayesian networks and Markov blankets discovery, and thus has many practical applications, ranging from fraud detection to clinical decision support. In this paper, we introduce a new distributed memory approach to the exact parent sets assignment problem. To achieve scalability, we derive theoretical bounds to constraint the search space when MDL scoring function is used, and we reorganize the underlying dynamic programming such that the computational density is increased and fine-grain synchronization is eliminated. We then design efficient realization of our approach in the Apache Spark platform. Through experimental results, we demonstrate that the method maintains strong scalability on a 500-core standalone Spark cluster, and it can be used to efficiently process data sets with 70 variables, far beyond the reach of the currently available solutions