Proceedings Editor:

One of the major applications of data mining is in helping companies determine which potential customers to market to. If the expected pro t from a customer is greater than the cost of marketing to her, the marketing action for that customer is executed. So far, work in this area has considered only the intrinsic value of the customer (i.e, the expected pro t from sales to her). We propose to model also the customer's network value: the expected pro t from sales to other customers she may inuence to buy, the customers those may inuence, and so on recursively. Instead of viewing a market as a set of independent entities, we view it as a social network and model it as a Markov random eld. We show the advantages of this approach using a social network mined from a collaborative ltering database. Marketing that exploits the network value of customers|also known as viral marketing|can be extremely eective, but is still a black art. Our work can be viewed as a step towards providing a more solid foundation for it, taking advantage of the availability of large relevant databases. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications| data mining

As the Internet develops into a robust channel for commerce, it will be important to understand the characteristics of electronic markets. Businesses, consumers, government regulators, and academic researchers face a variety of questions when analyzing these nascent markets. Will electronic markets have less friction than comparable conventional markets? What factors lead to dispersion in Internet prices? What are the major electronic commerce developments to watch in the coming years? This paper addresses these questions by reviewing current academic research, discussing the implications of this research, and proposing areas for future study. We review evidence that Internet markets are more efficient than conventional markets with respect to price levels, menu costs, and price elasticity. However, several studies find substantial and persistent dispersion in prices on the Internet. This price dispersion may be explained, in part, by heterogeneity in retailer-specific factors such as ...

Quality of service of e-commerce sites has been usually managed by the allocation of resources such as processors, disks, and network bandwidth, and by tracking conventional performance metrics such as response time, throughput, and availability. However, the metrics that are of utmost importance to the management of a Web store are revenue and profits. Thus, resource management schemes for e-commerce servers should be geared towards optimizing business metrics as opposed to conventional performance metrics. This paper introduces a state transition graph called Customer Behavior Model Graph (CBMG) to describe a customer session. It then presents a family of priority-based resource management policies for e-commerce servers. Priorities change dynamically as a function of the state a customer is in and as a function of the amount of money the customer has accumulated in his/her shopping cart. A detailed simulation model was developed to assess the gain of adaptive policies with respect t...

this paper, we discuss the design of algorithms for agents to use when participating in multiple simultaneous English auctions, aiming to purchase multiple goods. Firstly, we present a coordination algorithm, which ensures the agent places appropriate bids in the different auctions to buy exactly the right number of goods. Secondly, we combine this with an algorithm to determine what maximum bid an agent should place in an auction which is about to terminate. This algorithm combines a belief-based model of the auctions with a utility analysis. This analysis is to trade off the certain outcome of the terminating auction against the possible outcomes of the remaining auctions, and hence place appropriate bids in each

Internet-based distribution of mass-market content provides great opportunities for producers, distributors, and consumers, but it may seriously threaten users’ privacy. Some of the paths to loss of privacy are quite familiar (e.g., mining of credit-card data), but some are new or much more serious than they were in

authors wish to thank Comscore Media Metrix for their generous contribution of data without which this research would not have been possible. Additionally, we would like to thank Brett Gordon for his help with perl scripting, and Randy Bucklin, Ron Goettler, and Ajay Kalra for their comments.

Abstract. Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented perspective toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues—such as evaluation, targeting, and privacy and trust—which we also briefly address. Keywords: recommendation, recommender systems, small-worlds, social networks, user modeling “What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.”

E-commerce will undoubtedly change the way business is done. But as we have said elsewhere, “technology changes, economic laws do not. ” Despite the changes introduced by e-commerce, many of the fundamental principles of competition will still be relevant. In this paper I investigate three aspects of competition in ecommerce: marketing, interconnection, and price matching. In each case I will describe the phenomenon, illustrate its relevance for ecommerce, and describe some research issues raised.

Abstract. Business models on the basis of digital content require sophisticated descriptions of that content, as well as service-oriented carrier architectures that allow to negotiate and enforce contract and license schemes in heterogeneous digital application environments. We describe Knowledge Content Objects (KCO), that provide expressive semantic descriptions of digital content, based on an ontology of Information Objects, built under the DOLCE, DnS and Plan Ontologies (DDPO). In particular, we discuss how this structure supports business requirements within the context of paid content. Interactions between agents are embedded into digital infrastructures that are implemented on an advanced knowledge content carrier architecture (KCCA) that communicates via a dedicated protocol (KCTP). We show how this architecture allows to integrate existing digital repositories so that the content can be made available to a semantically rich digital environment. 1