Abstract not found

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

... In this paper we consider the use of implicit feedback techniques for query expansion and user profiling in information retrieval tasks. These techniques unobtrusively obtain information about users by watching their natural interactions with the system. Some of the user behaviors that have been most extensively investigated as sources of implicit feedback include reading time, saving, printing and selecting. The primary advantage to using implicit techniques is that such techniques remove the cost to the user of providing feedback. Implicit measures are generally thought to be less accurate than explicit measures [Nic97], but as large quantities of implicit data can be gathered at no extra cost to the user, they are attractive alternatives. Moreover, implicit measures can be combined with explicit ratings to obtain a more accurate representation of user interests. Implicit

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Recommender systems provide personalized suggestions about items that users will find interesting. Typically, recommender systems require a user interface that can "intelligently" determine the interest of a user and use this information to make suggestions. The common solution, "explicit ratings", where users tell the system what they think about a piece of information, is well-understood and fairly precise. However, having to stop to enter explicit ratings can alter normal patterns of browsing and reading. A more "intelligent " method is to use implicit ratings, where a rating is obtained by a method other than obtaining it directly from the user. These implicit interest indicators have obvious advantages, including removing the cost of the user rating, and that every user interaction with the system can contribute to an implicit rating. Current recommender systems mostly do not use implicit ratings, nor is the ability of implicit ratings to predict actual user interest well-understood. This research studies the correlation between various implicit ratings and the explicit rating for a single Web page. A Web browser was developed to record the user's actions (implicit ratings) and the explicit rating of a page. Actions included mouse clicks, mouse movement, scrolling and elapsed time. This browser was used by over 80 people that browsed more than 2500 Web pages. Using the data collected by the browser, the individual implicit ratings and some combinations of implicit ratings were analyzed and compared with the explicit rating. We found that the time spent on a page, the amount of scrolling on a page and the combination of time and scrolling had a strong correlation with explicit interest, while individual scrolling methods and mouse-clicks were ineffective in predicting explicit interest. 1

Collaborative filtering systems help address information overload by using the opinions of users in a community to make personal recommendations for documents to each user. Many collaborative filtering systems have few user opinions relative to the large number of documents available. This sparsity problem can reduce the utility of the filtering system by reducing the number of documents for which the system can make recommendations and adversely affecting the quality of recommendations. This paper defines and implements a model for integrating content-based ratings into a collaborative filtering system. The filterbot model allows collaborative filtering systems to address sparsity by tapping the strength of content filtering techniques. We identify and evaluate metrics for assessing the effectiveness of filterbots specifically, and filtering system enhancements in general. Finally, we experimentally validate the filterbot approach by showing that even simple filterbots such as spell ...

Social filtering systems that use explicit ratings require a large number of ratings to remain viable. The effort involved for a user to rate a document may outweigh any benefit received, leading to a shortage of ratings. One approach to this problem is to use implicit ratings: where user actions are recorded and a rating is inferred from the recorded data. This paper discusses the costs and benefits of using implicit ratings for information filtering applications.

Can implicit feedback substitute for explicit ratings in recommender systems? If so, we could avoid the difficulties associated with gathering explicit ratings from users. How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations? In this paper we identify three types of implicit feedback and suggest two strategies for using implicit feedback to make recommendations.

. Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a ma...

This paper has not been submitted elsewhere in identical or similar form, nor will it be during the first three months after its submission to UMUAI. As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results. Current work is investigating the interaction between the user profiles and conceptual search wherein documents are indexed by their concepts in addition to their keywords.