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Josef Fink

Bio: Josef Fink is an academic researcher from Center for Information Technology. The author has contributed to research in topics: User modeling & Adaptation (computer science). The author has an hindex of 10, co-authored 12 publications receiving 864 citations.

Papers
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Journal ArticleDOI
TL;DR: A presentation of the core features of these commercial systems may provide a source of information and inspiration for the design, implementation, and deployment of future user modeling systems in research and commercial environments.
Abstract: The aim of this article is to present and discuss selected commercial user modeling systems against the background of deployment requirements in real-world environments. Following the recent trend towards personalization on the World Wide Web, these systems are mainly aimed at supporting e-commerce including customer relationship management. In order to guide and structure our review, we define a requirements catalogue that comprises the main dimensions of functionality, data acquisition, representation, extensibility and flexibility, integration of external user-related information, compliance with standards, concern for privacy, and system architecture. Apart from the novelty of such a comparison both inside and outside the classical user modeling literature, a presentation of the core features of these commercial systems may provide a source of information and inspiration for the design, implementation, and deployment of future user modeling systems in research and commercial environments.

236 citations

Journal ArticleDOI
TL;DR: A user modeling server is described 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.
Abstract: 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.

198 citations

Journal ArticleDOI
TL;DR: This research is to verify that adaptation and user modeling techniques that were hitherto mostly used for catering interactive software systems to able-bodied users also prove useful for adaptation to users with special needs.
Abstract: Due to the rapidly increasing popularity of the World-Wide Web, hypermedia is going to be the leading online information medium for some years to come and will most likely become the standard gateway for citizens to the “information highway” Today, visitors of web sites are generally heterogeneous and have different needs, and this is likely to increase in the future The aim of the AVANTI project is to cater hypermedia information to these individual needs by adapting the content and the presentation of web pages to each individual user The special needs of elderly and disabled users are also partly considered A model of the characteristics of user groups, individual users and usage environments, and a domain model are exploited in the adaptation process One aim of this research is to verify that adaptation and user modeling techniques that were hitherto mostly used for catering interactive software systems to able-bodied users also prove useful for adaptation to users with special needs An

133 citations

01 Jan 1996
TL;DR: A model of the characteristics of user groups and individual users and a domain model are exploited in the adaptation process to cater to individual needs of visitors of web sites.
Abstract: Visitors of web sites are generally heterogeneous and have different needs. The aim of the AVANTI project is to cater to these individual needs by adapting the content and the presentation of web pages to each individual user. The special needs of elderly and handicapped users are also partly considered. A model of the characteristics of user groups and individual users and a domain model are exploited in the adaptation process.

80 citations

Book ChapterDOI
01 Jan 1997
TL;DR: The experience from this research is that adaptation and user modeling techniques that have so far almost exclusively focused on adapting interactive software systems to “normal” users also prove useful for adaptation to users with special needs.
Abstract: The tremendously increasing popularity of the World Wide Web indicates that hypermedia is going to be the leading online information medium for the years to come and will most likely be the standard gateway to the “information highway”. Visitors of web sites are generally heterogeneous and have different needs, and this trend is likely even to increase in the future. The aim of the AVANTI project is to cater hypermedia information to these different needs by adapting the content and the presentation of web pages to each individual user. The special needs of elderly and handicapped users are also considered to some extent. Our experience from this research is that adaptation and user modeling techniques that have so far almost exclusively focused on adapting interactive software systems to “normal” users also prove useful for adaptation to users with special needs.

65 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
27 Mar 2001
TL;DR: Adaptive hypermedia as mentioned in this paper is a relatively new direction of research on the crossroads of hypermedia and user modeling, which builds a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user.
Abstract: Adaptive hypermedia is a relatively new direction of research on the crossroads of hypermedia and user modeling. Adaptive hypermedia systems build a model of the goals, preferences and knowledge of each individual user, and use this model throughout the interaction with the user, in order to adapt to the needs of that user. The goal of this paper is to present the state of the art in adaptive hypermedia at the eve of the year 2000, and to highlight some prospects for the future. This paper attempts to serve both the newcomers and the experts in the area of adaptive hypermedia by building on an earlier comprehensive review (Brusilovsky, 1996; Brusilovsky, 1998).

1,842 citations

Patent
14 Sep 2010
TL;DR: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal role, is employed to define a scope or domain of operation, is proposed in this article, where user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.
Abstract: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal “role”, is employed to define a scope or domain of operation. The operation itself may be a database search, to interactively define a taxonomic context for the operation, a business negotiation, or other activity. After retrieval of results, a scoring or ranking may be applied according to user define criteria, which are, for example, commensurate with the relevance to the context, but may be, for example, by date, source, or other secondary criteria. A user profile is preferably stored in a computer accessible form, and may be used to provide a history of use, persistent customization, collaborative filtering and demographic information for the user. Advantageously, user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.

1,465 citations

Book ChapterDOI
01 Jan 2007
TL;DR: This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems by focusing on the overlay approach to user model representation and the uncertainty-based approach touser modeling.
Abstract: One distinctive feature of any adaptive system is the user model that represents essential information about each user This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user modeling

869 citations

Journal ArticleDOI
TL;DR: A review of the development of generic user modeling systems over the past twenty years is given in this article, which describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers.
Abstract: The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user. Several implemented research prototypes and commercial systems are briefly described.

711 citations