We introduce a new ascending auction that allocates heterogeneous objects among bidders with purely private unit demands. Our auction design differs from existing dynamic auctions in a number of ways: it economizes on information solicited from bidders by requiring marginal bidders to reveal a single new bid at a time; it uses a transparent price adjustment process; and it allows the seller to set starting prices above his reservation valuations. Despite these new features, (i) the auction stops in a finite time, (ii) sincere bidding is an ex-post Nash equilibrium, (iii) the auction ending prices and revenue depend only on bidders valuations and starting prices, and (iv) the auction is efficient if it starts with the seller’s valuations.
The rapid growth of the Internet, particularly the explosion of social media, has led to unprecedented increases in the volume of network data worldwide. Already, the Yahoo Web Graph collected in 2002 contains in excess of one billion URLs, the Facebook social network recently exceeded one billion users, and numerous other social networks or online communities easily claim memberships in the millions of users. One fundamental task towards understanding the structural and functional properties of large-scale networks is to detect its community structure, where each community consists of a group of (relatively) densely interconnected nodes. Recently, there has been growing interest in overlapping community detection due to the evidence of significant community overlaps found in large-scale real networks with ground-truth communities (Yang and Leskovec 2012). Not surprisingly, for example, it is generally accepted that actors in a social network tend to belong to multiple social groups (such as family, colleagues, and friends), depending on whom they are interacting with. The discovered communities can be explored and utilized in a number of important applications such as identifying fraudulent actions in telecommunication networks (Pinheiro 2012), studying dynamics of viral marketing (Leskovec et al. 2007), and identifying target groups in consumer networks (Hill et al. 2006). However, only a few algorithms have been successfully applied to large networks in excess of hundreds of millions of nodes — and to the best of our knowledge, none of them are based on a statistical framework.
We propose a novel method to identify predominant paths-to-purchase of retail consumers from activity level dataset collected in CRM systems. We verify the effectiveness of the proposed model on a simulated dataset. Following successful verification, we apply the model on a retail dataset from a major multi-channel, multi-brand North American Retailer. We uncover three different types of consumers based on how they respond to external stimuli over time: catalog driven shoppers, email driven shoppers, and holiday driven online shoppers. We also find significant activity across channels by these consumers. Finally, we use the path information in the segments to identify the groups that are most sensitive to a certain type of marketing contact. By analyzing the response of customers in different groups in a test dataset, we show that managers can optimize marketing budget allocation using our proposed segmentation approach.
In this era of big data, even though there exists an abundance of data documenting fashion and fashion trends, there has barely been any quantitative research conducted on the topic of influence or leadership. Unlike many other innovation domains such as patents where citations are explicit, a fashion designer hardly claims that s/he is influenced by others. To trace the hidden fashion influence network, we propose a novel approach to analyze the design influence in fashion industry by comparing similarity between designers in adopting same fashion symbols. Based on text processing techniques, we develop a quantitative model to extract fashion influences from 14-year historical data on fashion reviews. A total of 6,629 fashion runway reviews from the year 2000 to 2014 have been collected for analysis. We compared the performance of our proposed model with the globally published “most influential” lists and calculated a performance of 92.81% area under curve (AUC).
While recent research has suggested the tremendous potential of electronic health records (EHR) to transform healthcare, there remains a limited understanding of the best ways to utilize EHR data to improve clinical decision-making. Healthcare analytics based on EHR data may be able to offer a solution to the challenging goal of providing effective clinical decision support in chronic care. This paper takes a first step towards data-driven, evidence-based healthcare analytics in information systems research. Following the paradigms of design science and predictive analytics research, we propose, demonstrate and evaluate a design framework of risk prediction in the context of chronic disease management. Our framework draws on a large longitudinal real-world EHR dataset and evidence based guidelines to support data- and science-driven clinical decision making. We choose diabetes and coronary heart disease as our experimental cases, each with thousands of patients in their respective cohorts. The results of the experiments suggest that our design can achieve an accurate and reliable predictive performance and that the design is generalizable across chronic diseases. The design artifact and the experimental results contribute to the IS knowledge base and provide important theoretical and practical implications for design science, predictive analytics, and health IT research.
Prior research has shown that online recommendations have significant influence on users’ preference ratings and economic behavior. Specifically, the self-reported preference rating (for a specific consumed item) that is submitted by a user to a recommender system can be affected (i.e., distorted) by the previously observed system’s recommendation. As a result, anchoring (or anchoring-like) biases reflected in user ratings not only provide a distorted view of user preferences but also contaminate inputs of recommender systems, leading to decreased quality of future recommendations. This research explores two approaches to removing anchoring biases from self-reported consumer ratings. The first proposed approach is based on a computational post-hoc de-biasing algorithm that systematically adjusts the user-submitted ratings that are known to be biased. The second approach is a user-interface-driven solution that tries to minimize anchoring biases at rating collection time. Our empirical investigation explicitly demonstrates the impact of biased vs. unbiased ratings on recommender systems’ predictive performance. It also indicates that the post-hoc algorithmic de-biasing approach is very problematic, most likely due to the fact that the anchoring effects can manifest themselves very differently for different users and items. This further emphasizes the importance of proactively avoiding anchoring biases at the time of rating collection. Further, through laboratory experiments, we demonstrate that certain interface designs of recommender systems are more advantageous than others in effectively reducing anchoring biases.
Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web2.0 applications. The tags provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of ternary interaction data limits the performance of tag-based collaborative filtering. This paper proposes a random-walk-based algorithm to deal with the sparsity problem in social tagging data, which captures the potential transitive associations between users and items through their interaction with tags. In particular, two smoothing strategies are presented from both the user-centric and item-centric perspectives. Experiments on real-world data sets empirically demonstrate the efficacy of the proposed algorithm.
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud. Despite research on related topics, fake escrow website categorization has received little attention. In this study we evaluated the effectiveness of various features and techniques for detecting fake escrow websites. Our analysis included a rich set of features extracted from web page text, image, and link information. We also proposed a composite kernel tailored to represent the properties of fake websites, including content duplication and structural attributes. Experiments were conducted to assess the proposed features, techniques, and kernels on a test bed encompassing nearly 90,000 web pages derived from 410 legitimate and fake escrow sites. The combination of an extended feature set and the composite kernel attained over 98% accuracy when differentiating fake sites from real ones, using the support vector machines algorithm. The results suggest that automated web-based information systems for detecting fake escrow sites could be feasible and may be utilized as authentication mechanisms.
