Traditional recommendation systems make recommendations based solely on the customer's past purchases, product ratings and demographic data without considering the profitability the items being recommended. In this work we study the question of how a vendor can directly incorporate the profitability of items into its recommender so as to maximize its expected profit while still providing accurate recommendations. Our approach uses the output of any traditional recommender system and adjust them according to item profitabilities. Our approach is parameterized so the vendor can control how much the recommendation incorporating profits can deviate from the traditional recommendation. We study our approach under two settings and show that it achieves approximately 22% more profit than traditional recommendations.

We propose a number of techniques for obtaining a global ranking from data that may be incomplete and imbalanced -- characteristics almost universal to modern datasets coming from e-commerce and internet applications. We are primarily interested in score or rating-based cardinal data. From raw ranking data, we construct pairwise rankings, represented as edge flows on an appropriate graph. Our statistical ranking method uses the graph Helmholtzian, the graph theoretic analogue of the Helmholtz operator or vector Laplacian, in much the same way the graph Laplacian is an analogue of the Laplace operator or scalar Laplacian. We study the graph Helmholtzian using combinatorial Hodge theory: we show that every edge flow representing pairwise ranking can be resolved into two orthogonal components, a gradient flow that represents the L2-optimal global ranking and a divergence-free flow (cyclic) that measures the validity of the global ranking obtained -- if this is large, then the data does not have a meaningful global ranking. This divergence-free flow can be further decomposed orthogonally into a curl flow (locally cyclic) and a harmonic flow (locally acyclic but globally cyclic); these provides information on whether inconsistency arises locally or globally. An obvious advantage over the NP-hard Kemeny optimization is that discrete Hodge decomposition may be computed via a linear least squares regression. We also investigated the L1-projection of edge flows, showing that this is dual to correlation maximization over bounded divergence-free flows, and the L1-approximate sparse cyclic ranking, showing that this is dual to correlation maximization over bounded curl-free flows. We discuss relations with Kemeny optimization, Borda count, and Kendall-Smith consistency index from social choice theory and statistics.

We discuss experiences from evaluating the learning performance of a user-adaptive personal assistant agent.  We discuss the challenge of designing adequate evaluation and the tension of collecting adequate data without a fully functional, deployed system.  Reflections on negative and positive experiences point to the challenges of evaluating user-adaptive AI systems.  Lessons learned concern early consideration of evaluation and deployment, characteristics of AI technology and domains that make controlled evaluations appropriate or not, holistic experimental design, implications of "in the wild" evaluation, and the effect of AI-enabled functionality and its impact upon existing tools and work practices.

Collaborative filtering (CF) recommender systems are very popular and successful in commercial application fields. One end-user concern is the privacy of the personal data required by such systems in order to make personalized recommendations. Recently, peer-to-peer decentralized architectures have been proposed to address this privacy issue. On the other hand system managers must be concerned about system robustness. In particular, it has been shown that recommender systems are vulnerable to profile injection, although model-based CF algorithms show greater stability against malicious attacks that have been studied in the state-of-the-art. In this paper we generalize the generic model for decentralized recommendation and discuss the trade-off between robustness and privacy. In this context, we argue that exposing knowledge of the model parameters allows new, highly effective, model-based attack strategies to be considered. We conclude that the security concerns of privacy and robustness stand in opposition to each other and are difficult to satisfy simultaneously.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB). If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals. Thus we have developed an online,content-based recommender system, called SmartChoice. Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.

Public school choice at the primary and secondary levels is a keyelement of the U.S. No Child Left Behind Act of 2001 (NCLB).  If aschool does not meet assessment goals for two consecutive years, bylaw the district must offer students the opportunity to transfer to aschool that is meeting its goals.  Making a choice with such potentialimpact on a child's future is clearly monumental, yet astonishinglyfew parents take advantage of the opportunity.  Our research has shownthat a significant part of the problem arises from issues ininformation access and information overload, particularly for lowsocioeconomic status families.  Thus we have developed an online,content-based recommender system, called SmartChoice.  Itprovides parents with school recommendations for individual studentsbased on parents' preferences and students' needs, interests,abilities, and talents.  The first version of the online applicationwas deployed and live for focus group participants who used it for theJanuary and March/April 2008 Charlotte-Mecklenburg school choiceperiods.  This article describes the SmartChoice Program and theresults of our initial and followup studies with participants.

Eudaemonics is the study of the nature, causes, and conditions of human well-being. According to the ethical theory of eudaemonia, reaping satisfaction and fulfillment from life is not only a desirable end, but a moral responsibility. However, in modern society, many individuals struggle to meet this responsibility. Computational mechanisms could better enable individuals to achieve eudaemonia by yielding practical real-world systems that embody algorithms that promote human flourishing. This article presents eudaemonic systems as the evolutionary goal of the present day recommender system.

Since the Turing test was first proposed by Alan Turing in 1950, the primary goal of artificial intelligence has been predicated on the ability for computers to imitate human behavior. However, the majority of uses for the computer can be said to fall outside the domain of human abilities and it is exactly outside of this domain where computers have demonstrated their greatest contribution to intelligence. Another goal for artificial intelligence is one that is not predicated on human mimicry, but instead, on human amplification. This article surveys various systems that contribute to the advancement of human and social intelligence.

Interactive artificial intelligence systems employ preferences in both their reasoning and their interaction with the user. This survey considers preference handling in applications such as recommender systems, personal assistant agents, and personalized user interfaces. We survey the major questions and approaches, present illustrative examples, and give an outlook on potential benefits and challenges.

We address user system interaction issues in product search and recommender systems: how to help users select the most preferential item from a large collection of alternatives. As such systems must crucially rely on an accurate and complete model of user preferences, the acquisition of this model becomes the central subject of our paper. Many tools used today do not satisfactorily assist users to establish this model because they do not adequately focus on fundamental decision objectives, help them reveal hidden preferences, revise conflicting preferences, or explicitly reason about tradeoffs. In this article, we provide some analyses of common areas of design pitfalls and derive a set of design guidelines that assist the user in avoiding these problems in three important areas: user preference elicitation, preference revision, and explanation interfaces.