Data Mining Practical Machine Learning Tools and Techniques

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

An Introduction to MultiAgent Systems.

Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

/pdf/content-based-recommender-systems-state-of-the-art-and-4luq4392k8.pdf

Content-based Recommender Systems: State of the Art and Trends

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

/pdf/context-aware-recommender-systems-50xjes8kop.pdf

Context-Aware Recommender Systems

Intelligent adaptation is a key issue for the design of flexible support systems for mobile users. In this paper we present UbiquiTO, a tourist guide which integrates different forms of adaptation: (i) to the device used (web access via Laptop, PDA, smartphone), (ii) to the user and her features and preferences (personalized interaction), (iii) to the context of interaction, and in particular to the user location, besides some other features such as the time of the day. UbiquiTO adapts the content of the service being provided (recommendation and amount/type of information/features associated with each recommendation) and the presentation (interface). In order to achieve better performance it keeps track of the user behavior, updating and refining the user model during the interaction. In the paper we introduce the architecture of the system and the choices we made as regards user, device and context modeling and adaptation strategies. We also present the results of a preliminary evaluation of the system behavior.

Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide

This paper aims to demonstrate that the principles of adaptation and user modeling, especially social annotation, can be integrated fruitfully with those of the web 2.0 paradigm and thereby enhance in the domain of cultural heritage. We propose a framework for improving recommender systems through exploiting the users tagging activity. We maintain that web 2.0's participative features can be exploited by adaptive web-based systems in order to enrich and extend the user model, improve social navigation and enrich information from a bottom-up perspective. Thus our approach stresses social annotation as a new and powerful kind of feedback and as a way to infer knowledge about users. The prototype implementation of our framework in the domain of cultural heritage is named iCITY. It is serving to demonstrate the validity of our approach and to highlight the benefits of this approach specifically for cultural heritage. iCITY is an adaptive, social, multi-device recommender guide that provides information about the cultural resources and events promoting the cultural heritage in the city of Torino. Our paper first describes this system and then discusses the results of a set of evaluations that were carried out at different stages of the systems development and aimed at validating the framework and implementation of this specific prototype. In particular, we carried out a heuristic evaluation and two sets of usability tests, aimed at checking the usability of the user interface, specifically of the adaptive behavior of the system. Moreover, we conducted evaluations aimed at investigating the role of tags in the definition of the user model and the impact of tags on the accuracy of recommendations. Our results are encouraging.

/pdf/tag-based-user-modeling-for-social-multi-device-adaptive-510xwlhdjt.pdf

Tag-based user modeling for social multi-device adaptive guides

Personalization is one of the keys for the success of web services. In this paper we present SeAN (Server for Adaptive News), an adaptive system for the personalized access to news servers on the WWW. The aims of the system are (i) to select the sections (topics) and news in the server that are most relevant for each user, (ii) to customize the detail level of each news item to the user's characteristics and (iii) to select the advertisements that are most appropriate for each page and user. In the paper we discuss the functionalities of the system and we present the choices we made in its design. In particular, we focus on the techniques we adopted for structuring the news archive, for creating and maintaining the user model and for generating the personalized hypertext for browsing the news server.

An adaptive system for the personalized access to news

Social tagging is a kind of social annotation by which users label resources, typically web objects, by means of keywords with the goal of sharing, discovering and recovering them. In this paper we investigate the possibility of exploiting the user tagging activity in order to infer knowledge about the user. Up to now the relation between tagging and user modeling seems not to have been investigated in depth. Given the widespread diffusion of web tools for collaborative tagging, it is interesting to understand how user modeling can benefit from this feedback.

Towards a Tag-Based User Model: How Can User Model Benefit from Tags?

In this paper we provide a classification of adaptive systems with respect to the kind of semantic technology they exploit to accomplish or improve specific adaptation and user modeling tasks. This classification is based on a distinction between strong semantic techniques and weak semantic techniques. The former are techniques based on the Semantic Web, while the latter regard technologies that, in different ways, annotate resources, enriching their meaning. This second category includes, in particular, Web 2.0 social annotations and mixed approaches between social annotations and Semantic Web techniques. While the impact of the Semantic Web on adaptive systems has been discussed in several survey papers, the potential of weak semantic technologies has, so far, received little attention. The aim of this analysis is to fill this gap. Therefore, we will discuss contributions and limits of both approaches, but we will focus special attention on weak semantic adaptive systems.

Ilaria Torre

Papers

Integrating heterogeneous adaptation techniques to build a flexible and usable mobile tourist guide

Tag-based user modeling for social multi-device adaptive guides

An adaptive system for the personalized access to news

Towards a Tag-Based User Model: How Can User Model Benefit from Tags?

Adaptive systems in the era of the semantic and social web, a survey