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Algorithms are increasingly making decisions regarding what news articles should be shown to online users. In recent times, unhealthy outcomes from these systems have been highlighted including their vulnerability to amplifying small differences and offering less choice to readers. In this paper we present and study a new class of feedback models that exhibit a variety of self-organizing behaviors. In addition to showing important emergent properties, our model generalizes the popular "top-N news recommender systems" in a manner that provides media managers a mechanism to guide the emergent outcomes to mitigate potentially unhealthy outcomes driven by the self-organizing dynamics. We use complex adaptive systems framework to model the popularity evolution of news articles. In particular, we use agent-based simulation to model a reader's behavior at the microscopic level and study the impact of various simulation hyperparameters on overall emergent phenomena. This simulation exercise enables us to show how the feedback model can be used as an alternative recommender to conventional top-N systems. Finally, we present a design framework for multi-objective evolutionary optimization that enables recommendation systems to co-evolve with the changing online news readership landscape., Competing Interests: The authors have declared that no competing interests exist.

Abstract. Fraud, waste, and abuse are significant problems in major industries such as healthcare, particularly when third-party payers such as Medicare are involved. Auditors looking for fraudulent activities use scoring [...]

Abroad motivation for our research is to build manipulation resistant news recommender systems. There are several algorithms that can be used to generate news recommendations, and the strategies for manipulation [...]

Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GHIC, that groups customer transactions such that itemsets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions. Index Terms--Data mining, clustering, classification, association rules, Web mining.

Recent developments in artificial intelligence (AI) have increased interest in combining AI with human intelligence to develop superior systems that augment human and artificial intelligence. In this paper, augmented intelligence informally means computers and humans working together, by design, to enhance one another, such that the intelligence of the resulting system improves. Intelligence augmentation (IA) can pool the joint intelligence of humans and computers to transform individual work, organizations, and society. Notably, applications of IA are beginning to emerge in several domains, such as cybersecurity, privacy, counterterrorism, and healthcare, among others. We provide a brief summary of papers in this special section that represent early attempts to address some of the rapidly emerging research issues. We also present a framework to guide research on IA and advocate for the important implications of IA for the future of work, organizations, and society. We conclude by outlining promising research directions based on this framework for the information systems and related disciplines. [ABSTRACT FROM AUTHOR]

We address the discovery of periodic patterns in sequence data. Building on prior work in this area, we W present definitions and new methods for characterizing and identifying four types of periodic patterns. A unifying concept across the different types of periodic patterns we consider is the use of statistical variance to define periodicity. This lends itself to efficient variance-reduction algorithms for identifying periodic patterns. We motivate and test our approach using both extensive simulated sequences and real sequence data from online clickstream data. [ABSTRACT FROM AUTHOR]

A drawback of traditional data-mining methods is that they do not leverage prior knowledge of users. In prior work, we proposed a method that could discover unexpected patterns in data by using domain knowledge in a systematic manner. In this paper, we present new methods for discovering a minimal set of unexpected patterns by combining the two independent concepts of minimality and unexpectedness, both of which have been well-studied in the KDD literature. We demonstrate the strengths of this approach experimentally using a case study in a marketing domain. [ABSTRACT FROM AUTHOR]

Grouping customer transactions into segments may help understand customers better. The marketing literature has concentrated on identifying important segmentation variables (e.g., customer loyalty) and on using cluster analysis and mixture models for segmentation. The data mining literature has provided various clustering algorithms for segmentation without focusing specifically on clustering customer transactions. Building on the notion that observable customer transactions are generated by latent behavioral traits, in this paper, we investigate using a pattern-based clustering approach to grouping customer transactions. We define an objective function that we maximize in order to achieve a good clustering of customer transactions and present an algorithm, GI-IIC, that groups customer transactions such that itemsets generated from each cluster, while similar to each other, are different from ones generated from others. We present experimental results from user-centric Web usage data that demonstrates that GHIC generates a highly effective clustering of transactions. [ABSTRACT FROM AUTHOR]