595 research outputs found
Modeling Recommender Ecosystems: Research Challenges at the Intersection of Mechanism Design, Reinforcement Learning and Generative Models
Modern recommender systems lie at the heart of complex ecosystems that couple
the behavior of users, content providers, advertisers, and other actors.
Despite this, the focus of the majority of recommender research -- and most
practical recommenders of any import -- is on the local, myopic optimization of
the recommendations made to individual users. This comes at a significant cost
to the long-term utility that recommenders could generate for its users. We
argue that explicitly modeling the incentives and behaviors of all actors in
the system -- and the interactions among them induced by the recommender's
policy -- is strictly necessary if one is to maximize the value the system
brings to these actors and improve overall ecosystem "health". Doing so
requires: optimization over long horizons using techniques such as
reinforcement learning; making inevitable tradeoffs in the utility that can be
generated for different actors using the methods of social choice; reducing
information asymmetry, while accounting for incentives and strategic behavior,
using the tools of mechanism design; better modeling of both user and
item-provider behaviors by incorporating notions from behavioral economics and
psychology; and exploiting recent advances in generative and foundation models
to make these mechanisms interpretable and actionable. We propose a conceptual
framework that encompasses these elements, and articulate a number of research
challenges that emerge at the intersection of these different disciplines
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
Cloud computing as a fairly new commercial paradigm, widely investigated by
different researchers, already has a great range of challenges. Pricing is a
major problem in Cloud computing marketplace; as providers are competing to
attract more customers without knowing the pricing policies of each other. To
overcome this lack of knowledge, we model their competition by an
incomplete-information game. Considering the issue, this work proposes a
pricing policy related to the regret minimization algorithm and applies it to
the considered incomplete-information game. Based on the competition based
marketplace of the Cloud, providers update the distribution of their strategies
using the experienced regret. The idea of iteratively applying the algorithm
for updating probabilities of strategies causes the regret get minimized
faster. The experimental results show much more increase in profits of the
providers in comparison with other pricing policies. Besides, the efficiency of
a variety of regret minimization techniques in a simulated marketplace of Cloud
are discussed which have not been observed in the studied literature. Moreover,
return on investment of providers in considered organizations is studied and
promising results appeared
Pricing and Electric Vehicle Charging Equilibria
We study equilibria in an Electric Vehicle (EV) charging game, a cost
minimization game inherent to decentralized charging control strategy for EV
power demand management. In our model, each user optimizes its total cost which
is sum of direct power cost and the indirect dissatisfaction cost. We show
that, taking player specific price independent dissatisfaction cost in to
account, contrary to popular belief, herding only happens at lower EV uptake.
Moreover, this is true for both linear and logistic dissatisfaction functions.
We study the question of existence of price profiles to induce a desired
equilibrium. We define two types of equilibria, distributed and non-distributed
equilibria, and show that under logistic dissatisfaction, only non-distributed
equilibria are possible by feasibly setting prices. In linear case, both type
of equilibria are possible but price discrimination is necessary to induce
distributed equilibria. Finally, we show that in the case of symmetric EV
users, mediation cannot improve upon Nash equilibria
Preference Learning
This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies
Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction with Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces
© 2012 IEEE. The unprecedented increase in data volume has become a severe challenge for conventional patterns of data mining and learning systems tasked with handling big data. The recently introduced Spark platform is a new processing method for big data analysis and related learning systems, which has attracted increasing attention from both the scientific community and industry. In this paper, we propose a shared nearest-neighbor quantum game-based attribute reduction (SNNQGAR) algorithm that incorporates the hierarchical coevolutionary Spark model. We first present a shared coevolutionary nearest-neighbor hierarchy with self-evolving compensation that considers the features of nearest-neighborhood attribute subsets and calculates the similarity between attribute subsets according to the shared neighbor information of attribute sample points. We then present a novel attribute weight tensor model to generate ranking vectors of attributes and apply them to balance the relative contributions of different neighborhood attribute subsets. To optimize the model, we propose an embedded quantum equilibrium game paradigm (QEGP) to ensure that noisy attributes do not degrade the big data reduction results. A combination of the hierarchical coevolutionary Spark model and an improved MapReduce framework is then constructed that it can better parallelize the SNNQGAR to efficiently determine the preferred reduction solutions of the distributed attribute subsets. The experimental comparisons demonstrate the superior performance of the SNNQGAR, which outperforms most of the state-of-the-art attribute reduction algorithms. Moreover, the results indicate that the SNNQGAR can be successfully applied to segment overlapping and interdependent fuzzy cerebral tissues, and it exhibits a stable and consistent segmentation performance for neonatal cerebral cortical surfaces
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