User Modeling Servers Research Articles

Novel User Modeling Approaches for Personalized Learning Environments

Modeling user knowledge and creating user profiles not only for special web-based social media but also for complex and mixed personalized learning environments are important research challenges. The key component for adaptation is the user’s knowledge model. This paper introduces fuzzy metric (FM)-based novel and efficient similarity measurement method and adaptive artificial neural network (AANN) and artificial bee colony (ABC)-based knowledge classification approaches for personalized learning environments. For this purpose, FM-based method has been developed to measure distances more efficiently among the users and their knowledge model using the web logs/session data. In addition, a novel knowledge classifier based on ABC and AANN having combined with the generic object model has been developed for user modeling strategies and user modeling server of adaptive educational electric course (AEEC). Finally, the approaches have been tested to compare the classification performance of the user modeling methods developed for user modeling task. The experimental results have shown that proposed methods have improved similarity measurements considerably and decreased the misclassifications in user modeling processes. Thus, powerful user modeling approaches have been presented to the literature. It is expected that the approaches introduced in this article can be a reference to others researches and to develop more adaptive and personalized web applications in future.

A framework for performance evaluation of user modeling servers for Web applications

Adaptive Web systems utilize user models (or group models) to represent essential information about an individual user (or a group) so that these systems can adapt their behavior to the goals, tasks, interests, and other features of individual users or groups of users. Meanwhile, proper performance assessment has become a critical issue for the efficient deployment of adaptive Web systems that implement complex user model inferences. This paper presents one of the first efforts to develop a framework for evaluating the performance of user modeling servers (UMS). We conduct a performance-driven analysis of the UMS conceptual model, extracting a comprehensive set of parameters in order to build a practical UMS Performance Evaluation Framework (UMS/PEF). We also apply the proposed UMS/PEF framework for comparing performances between different UMS. Experimental results have demonstrated high utility of the proposed UMS/PEF framework. This work was partially supported by NSF CRCD/EI Award 0426021 and NSF CAREER Award 0447083.

An LDAP-based User Modeling Server and its Evaluation

Representation components of user modeling servers have been traditionally based on simple file structures and database systems. We propose directory systems as an alternative, which offer numerous advantages over the more traditional approaches: international vendor-independent standardization, demonstrated performance and scalability, dynamic and transparent management of distributed information, built-in replication and synchronization, a rich number of pre-defined types of user information, and extensibility of the core representation language for new information types and for data types with associated semantics. Directories also allow for the virtual centralization of distributed user models and their selective centralized replication if better performance is needed. We present UMS, a user modeling server that is based on the Lightweight Directory Access Protocol (LDAP). UMS allows for the representation of different models (such as user and usage profiles, and system and service models), and for the attachment of arbitrary components that perform user modeling tasks upon these models. External clients such as user-adaptive applications can submit and retrieve information about users. We describe a simulation experiment to test the runtime performance of this server, and present a theory of how the parameters of such an experiment can be derived from empirical web usage research. The results show that the performance of UMS meets the requirements of current small and medium websites already on very modest hardware platforms, and those of very large websites in an entry-level business server configuration.

Privacy through pseudonymity in user-adaptive systems

User-adaptive applications cater to the needs of each individual computer user, taking for example users' interests, level of expertise, preferences, perceptual and motoric abilities, and the usage environment into account. Central user modeling servers collect and process the information about users that different user-adaptive systems require to personalize their user interaction.Adaptive systems are generally better able to cater to users the more data their user modeling systems collect and process about them. They therefore gather as much data as possible and "lay them in stock" for possible future usage. Moreover, data collection usually takes place without users' initiative and sometimes even without their awareness, in order not to cause distraction. Both is in conflict with users' privacy concerns that became manifest in numerous recent consumer polls, and with data protection laws and guidelines that call for parsimony, purpose-orientation, and user notification or user consent when personal data are collected and processed.This article discusses security requirements to guarantee privacy in user-adaptive systems and explores ways to keep users anonymous while fully preserving personalized interaction with them. User anonymization in personalized systems goes beyond current models in that not only users must remain anonymous, but also the user modeling system that maintains their personal data. Moreover, users' trust in anonymity can be expected to lead to more extensive and frank interaction, hence to more and better data about the user, and thus to better personalization. A reference model for pseudonymous and secure user modeling is presented that meets many of the proposed requirements.

User Modeling for Personalized City Tours

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.

A Review and Analysis of Commercial User Modeling Servers for Personalization on the World Wide Web

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.

Adaptable and adaptive information provision for all users, including disabled and elderly people

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. Another original aspect is the development of a network-wide user modeling server that can concurrently accommodate the user modeling needs of several applications and several instances of an application within a distributed computing environment.