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.

/pdf/the-structure-and-function-of-complex-networks-34deompc9l.pdf

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

This chapter contains sections titled: 1. Introduction, 2. Economics of Bundling, 3. N-Good Bundling Model, 4. Modeling Heterogeneity in Consumer Preferences, 5. Production Costs and Economies of Scale, 6. Analysis and Empirical Results, 7. Conclusions, Notes, References

Network Delivery of Information Goods: Optimal Pricing of Articles and Subscriptions

Distortion of preferences and the Nash theory of bargaining

This is a review of various economic phenomenon that are important in high-technology industries, such as personalization of products and pricing, versioning, bundling, switching costs, lock-in, economics of scale, network eects, complements and computer-mediated contracts. Most of these forces tend to point towards higher industry concentration rations for technology-intensive industries.

/pdf/high-technology-industries-and-market-structure-36vdbwr5cn.pdf

High-Technology Industries and Market Structure

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/29701/0000033.pdf;sequence=1

Uniqueness of Nash equilibrium in private provision of public goods : An improved proof

https://deepblue.lib.umich.edu/bitstream/handle/2027.42/27112/0000104.pdf;sequence=1

Hal R. Varian

Papers

Network Delivery of Information Goods: Optimal Pricing of Articles and Subscriptions

Distortion of preferences and the Nash theory of bargaining

High-Technology Industries and Market Structure

Uniqueness of Nash equilibrium in private provision of public goods : An improved proof

Revealed Preference with a Subset of Goods