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Group modeling: Selecting a sequence of television items to suit a group of viewers. User Modeling and UserAdapted Interaction
, 2004
"... Abstract. Watching television tends to be a social activity. So, adaptive television needs to adapt to groups of users rather than to individual users. In this paper, we discuss different strategies for combining individual user models to adapt to groups, some of which are inspired by Social Choice ..."
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Abstract. Watching television tends to be a social activity. So, adaptive television needs to adapt to groups of users rather than to individual users. In this paper, we discuss different strategies for combining individual user models to adapt to groups, some of which are inspired by Social Choice Theory. In a first experiment, we explore how humans select a sequence of items for a group to watch, based on data about the individuals’ preferences. The results show that humans use some of the strategies such as the Average Strategy (a.k.a. Additive Utilitarian), the Average Without Misery Strategy and the Least Misery Strategy, and care about fairness and avoiding individual misery. In a second experiment, we investigate how satisfied people believe they would be with sequences chosen by different strategies, and how their satisfaction corresponds with that predicted by a number of satisfaction functions. The results show that subjects use normalization, deduct misery, and use the ratings in a nonlinear way. One of the satisfaction functions produced reasonable, though not completely correct predictions. According to our subjects, the sequences produced by five strategies give satisfaction to all individuals in the group. The results also show that subjects put more emphasis than expected on showing the best rated item to each individual (at a cost of misery for another individual), and that the ratings of the first and last items in the sequence are especially important. In a final experiment, we explore the influence viewing an item can have on the ratings of other items. This is important for deciding the order in which to present items. The results show an effect of both mood and topical relatedness.
Some Topics in Analysis of Boolean Functions
"... This article accompanies a tutorial talk given at the 40th ACM STOC conference. In it, we give a brief introduction to Fourier analysis of boolean functions and then discuss some applications: Arrow’s Theorem and other ideas from the theory of Social Choice; the BonamiBeckner Inequality as an exten ..."
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Cited by 44 (0 self)
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This article accompanies a tutorial talk given at the 40th ACM STOC conference. In it, we give a brief introduction to Fourier analysis of boolean functions and then discuss some applications: Arrow’s Theorem and other ideas from the theory of Social Choice; the BonamiBeckner Inequality as an extension of Chernoff/Hoeffding bounds to higherdegree polynomials; and, hardness for approximation algorithms.
Operators and Laws for Combining Preference Relations
, 2002
"... The paper is a theoretical study of a generalization of the lexicographic rule for combining ordering relations. We define the concept of priority operator: a priority operator maps a family of relations to a single relation which represents their lexicographic combination according to a certain pri ..."
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Cited by 43 (0 self)
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The paper is a theoretical study of a generalization of the lexicographic rule for combining ordering relations. We define the concept of priority operator: a priority operator maps a family of relations to a single relation which represents their lexicographic combination according to a certain priority on the family of relations. We present four kinds of results. We show
Statistical ranking and combinatorial Hodge theory
 Mathematical Programming
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F.: Group recommendations with rank aggregation and collaborative filtering
 In: RecSys 2010: Proceedings of the 2010 ACM Conference on Recommender Systems
, 2010
"... The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendatio ..."
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Cited by 36 (2 self)
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The majority of recommender systems are designed to make recommendations for individual users. However, in some circumstances the items to be selected are not intended for personal usage but for a group; e.g., a DVD could be watched by a group of friends. In order to generate effective recommendations for a group the system must satisfy, as much as possible, the individual preferences of the group’s members. This paper analyzes the effectiveness of group recommendations obtained aggregating the individual lists of recommendations produced by a collaborative filtering system. We compare the effectiveness of individual and group recommendation lists using normalized discounted cumulative gain. It is observed that the effectiveness of a group recommendation does not necessarily decrease when the group size grows. Moreover, when individual recommendations are not effective a user could obtain better suggestions looking at the group recommendations. Finally, it is shown that the more alike the users in the group are, the more effective the group recommendations are.
A brief introduction to Fourier analysis on the Boolean cube
 Theory of Computing Library– Graduate Surveys
, 2008
"... Abstract: We give a brief introduction to the basic notions of Fourier analysis on the ..."
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Cited by 34 (4 self)
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Abstract: We give a brief introduction to the basic notions of Fourier analysis on the
Inkpen: The Corporate Objective Revisited
 Organization Science
, 2004
"... T he stock market convulsions and corporate scandals of 2001 and 2002 have reignited debate on the purposes of the corporation and, in particular, the goal of shareholder value maximization. We revisit the debate, reexamine the traditional rationales, and develop a set of new arguments for why the ..."
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T he stock market convulsions and corporate scandals of 2001 and 2002 have reignited debate on the purposes of the corporation and, in particular, the goal of shareholder value maximization. We revisit the debate, reexamine the traditional rationales, and develop a set of new arguments for why the preferred objective function for the corporation must unambiguously continue to be the one that says "maximize shareholder value." We trace the origins of the debates from the late nineteenth century, their implications for accepted law and practice of corporate governance in the United States, and their reflection in shareholder versus stakeholder views in the organization studies literature and contractarian versus communitarian views in the legal literature. We address in detail possible critiques of the shareholder value maximization view. Although we recognize certain boundary constraints to our arguments, we conclude that the issues raised by such critiques and constraints are not unique to the shareholder value maximization view, but will exist even if the firm is managed on behalf of nonshareowning stakeholders.
Budgeted Social Choice: From Consensus to Personalized Decision Making
 PROCEEDINGS OF THE TWENTYSECOND INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 2011
"... We develop a general framework for social choice problems in which a limited number of alternatives can be recommended to an agent population. In our budgeted social choice model, this limit is determined by a budget, capturing problems that arise naturally in a variety of contexts, and spanning the ..."
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Cited by 31 (6 self)
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We develop a general framework for social choice problems in which a limited number of alternatives can be recommended to an agent population. In our budgeted social choice model, this limit is determined by a budget, capturing problems that arise naturally in a variety of contexts, and spanning the continuum from pure consensus decision making (i.e., standard social choice) to fully personalized recommendation. Our approach applies a form of segmentation to social choice problems— requiring the selection of diverse options tailored to different agent types—and generalizes certain multiwinner election schemes. We show that standard rank aggregation methods perform poorly, and that optimization in our model is NPcomplete; but we develop fast greedy algorithms with some theoretical guarantees. Experiments on realworld datasets demonstrate the effectiveness of our algorithms.