Abstract not found

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.

The explosive growth of the Internet and other sources of networked information have made automatic mediation of access to networked information sources an increasingly important problem. Much of this information

Several current support systems for travel and tourism are aimed at providing information in a personalized manner, taking users interests and preferences into account. In this vein, personalized systems observe users behavior and, based thereon, make generalizations and predictions about them. This article describes a user modeling server that offers services to personalized systems with regard to the analysis of user actions, the representation of assumptions about the user, and the inference of additional assumptions based on domain knowledge and characteristics of similar users. The system is open and compliant with major standards, allowing it to be easily accessed by clients that need personalization services.

Although tacit knowledge constitutes the major part of what we know, it is difficult for organisations to fully benefit from this valuable asset. This is because tacit knowledge is inherently elusive , and in order to capture, store, and disseminate it, it is argued that it first has to be made explicit.

The increasingly ubiquitous global information structure makes it possible to examine high-volume text streams that contain documents written in a variety of languages. Present monolingual adaptive filtering techniques learn profiles which reectuser preferences and then apply those profiles to reduce the volume of new documents that must be examined by the user to manageable levels. This paper presents three techniques for extending adaptive monolingual text filtering techniques to manage multilingual document streams. Experimental results are given which demonstrate that dictionary-based and corpus-based techniques achieve similar performance in this application. This observation motivates our development of a translation technique designed specifically for vector space text representations which can in principle exploit both dictionary-based and corpus-based techniques. Results of initial experiments with this technique are given and the potential advantages of the new technique are discussed. The paper concludes with a discussion of future directions for adaptive multilingual text fitering.

Information retrieval systems offer an attractive alternative to construction of a topic tracking system from scratch. The freely available PRISE vector space text retrieval system was applied to the TDT-2 topic tracking task. A simple version of the Rocchio formula was used for profile formulation and a retrieval status value threshold was used in conjunction with a temporal cutoff to make hard decisions. The results indicate that our simple approach produced a credible system, but comparison with results achieved by other systems indicates that there is room for improvement. The paper concludes by identifying some promising directions for further work that would be compatible with our approach. 1.

. Adaptive information filtering agents have been developed to alleviate the problem of information overload on the Internet by learning a user's changing information needs and autonomously scanning through the incoming stream of information according to the user's latest information needs. However, the explanatory power and the learning autonomy of these agents should be improved. Applying a logic-based framework for representation, learning, and matching in adaptive information filtering agents is promising since users' changing information needs can automatically be deduced by the agents. In addition, the inferred changes can be explained and justified based on formal deduction. This paper proposes a logic-based model for adaptive information filtering agents. Particularly, it illustrates how the AGM paradigm of belief revision can be applied to the learning processes of these agents. The impact of a logic-based framework on agents' learning autonomy and explanatory power ...

This paper is a review of the field of computer-supported cooperative work (CSCW) with respect to digital libraries. The literature surveyed covers both library & information science and computer science. An overview of the field of CSCW is provided including requirements capture, ethnography, interfaces, toolkits, organisational memory etc. Collaboration is interpreted in a wide sense and systems supporting user-staff (e.g. remote reference) and user-user (e.g. collaborative filtering) interactions are described.

Personalized classification refers to allowing users to define their own categories and automating the assignment of documents to these categories. In this paper, we examine the use of keywords to define personalized categories and propose the use of Support Vector Machine (SVM) to perform personalized classification. Two scenarios have been investigated. The first assumes that the personalized categories are defined in a flat category space. The second assumes that each personalized category is defined within a pre-defined general category that provides a more specific context for the personalized category. The training documents for personalized categories are obtained from a training document pool using a search engine and a set of keywords. Our experiments have delivered better classification results using the second scenario. We also conclude that the number of keywords used can be very small and increasing them does not always lead to better classification performance.