Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

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The Structure and Function of Complex Networks

Recommender systems apply knowledge discovery techniques to the problem of making personalized recommendations for information, products or services during a live interaction. These systems, especially the k-nearest neighbor collaborative ltering based ones, are achieving widespread success on the Web. The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. These are: producing high quality recommendations, performing many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. In traditional collaborative ltering systems the amount of work increases with the number of participants in the system. New recommender system technologies are needed that can quickly produce high quality recommendations, even for very large-scale problems. To address these issues we have explored item-based collaborative ltering techniques. Item-based techniques rst analyze the user-item matrix to identify relationships between di erent items, and then use these relationships to indirectly compute recommendations for users. In this paper we analyze di erent item-based recommendation generation algorithms. We look into di erent techniques for computing item-item similarities (e.g., item-item correlation vs. cosine similarities between item vectors) and di erent techniques for obtaining recommendations from them (e.g., weighted sum vs. regression model). Finally, we experimentally evaluate our results and compare them to the basic k-nearest neighbor approach. Our experiments suggest that item-based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

/pdf/item-based-collaborative-filtering-recommendation-algorithms-55n6bcyed4.pdf

Item-based collaborative filtering recommendation algorithms

PROBLEM TO BE SOLVED: To solve the problem, wherein it is impossible for an electronic content information provider to provide commercially secure and effective method, for a configurable general-purpose electronic commercial transaction/distribution control system. SOLUTION: In this system, having at least one protected processing environment for safely controlling at least one portion of decoding of digital information, a secure content distribution method comprises a process for encapsulating digital information in one or more digital containers; a process for encrypting at least a portion of digital information; a process for associating at least partially secure control information for managing interactions with encrypted digital information and/or digital container; a process for delivering one or more digital containers to a digital information user; and a process for using a protected processing environment, for safely controlling at least a portion of the decoding of the digital information. COPYRIGHT: (C)2006,JPO&NCIPI

https://apps.dtic.mil/dtic/tr/fulltext/u2/a423264.pdf

Systems and Methods for Secure Transaction Management and Electronic Rights Protection

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.

/pdf/xen-and-the-art-of-virtualization-vt3kaikb6y.pdf

Xen and the art of virtualization

http://www.bmcommunity.sitew.com/fs/Root/8jig8-businessmodelsbusinessstrategy.pdf

Business Models, Business Strategy and Innovation

The economics of communication and information

Economics and Electronic Access to Scholarly Information

Coase, competition, and compensation

Consumers often share intellectual property. Sometimes sharing is facilitated by intermediaries such as libraries, license servers, used-book shops or video rental stores. Sometimes sharing is illicit, such as with pirated software or Napster. Sometimes sellers of intellectual property welcome sharing, as with site licenses or special prices for libraries, and sometimes they discourage it. Intellectual property that is intended to be shared normally sells for a higher price than intellectual property that is meant to be consumed by individuals. Think, for example, of the differential pricing of journal subscriptions for libraries and individual users. In other cases, such as books, sellers cannot easily discriminate between shared and individual users, so pricing tends to reflect the dominant use. I have examined the pricing behavior of profit-maximizing sellers of intellectual property when sharing is possible in Varian [2000]. Here I examine a related question: what kinds of products are not produced due to sharing? That is, what is the social cost of sharing?

/pdf/the-social-cost-of-sharing-b2fmmdxex6.pdf

The Social Cost of Sharing

Economists have investigated models of optimal search behavior that may be relevant to information retrieval. This talk will describe some of the reults of this research and speculate on how they might be useful in the IR context. Professor Varian has published numerous papers in economic theory, industrial organization, financial economics, econometrics and information economics. He is the author of two major economics textbooks which have been translated into 9 languages. His recent work has been concerned with the economics of information technology and the information economy. He recently co-authored Information Rules (with Carl Shapiro, Harvard Business School Press 1998), a book which distills the economic principles of information and networks into practical business strategies. Information Rules was named Editor's Choice as one of the best business books of the year at Amazon.com

Hal R. Varian

Papers

The economics of communication and information

Economics and Electronic Access to Scholarly Information

Coase, competition, and compensation

The Social Cost of Sharing

The economics of search