Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

The Partial Evaluation Approach to Information Personalization

Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology - PIPE (`Personalization is Partial Evaluation') - for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a conceptual level and outline representational choices. We present two application case studies that use PIPE for personalizing web sites and describe how PIPE suggests a novel evaluation criterion for information system designs. Finally, we mention several fundamental implications of adopting the PIPE model for personalization and when it is (and is not) applicable.Comment: Comprehensive overview of the PIPE model for personalizatio

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

Automated user modeling for personalized digital libraries

Digital libraries (DL) have become one of the most typical ways of accessing any kind of digitalized information. Due to this key role, users welcome any improvements on the services they receive from digital libraries. One trend used to improve digital services is through personalization. Up to now, the most common approach for personalization in digital libraries has been user-driven. Nevertheless, the design of efficient personalized services has to be done, at least in part, in an automatic way. In this context, machine learning techniques automate the process of constructing user models. This paper proposes a new approach to construct digital libraries that satisfy user’s necessity for information: Adaptive Digital Libraries, libraries that automatically learn user preferences and goals and personalize their interaction using this information

The role of human factors in stereotyping behavior and perception of digital library users: A robust clustering approach

To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception

IR issues for digital ecosystems users

The purpose of this research is to discuss some challenges of information retrieval, especially Web information retrieval, in digital ecosystems from a user?s perspective. As a dominant search tool, search engines usually return millions of search results in a long flat list in which many or even most of the results can be irrelevant. The long flat list conveys nothing about knowledge structure related to the retrieved results and personal search preferences and interests are not explored.Although some search engines try to cluster the Web results, the automatically formed titles and knowledge hierarchy is prone to mismatching the searcher?s human mental model. In digital ecosystems, while many different search tools are available, they are not integrated. To address these issues, a search framework which combines categorization, clustering, ontology, and personalization is proposed, and thus the quality of search results in digital ecosystems is expected to be boosted

Modeling interactive memex-like applications based on self-modifiable petri nets

This paper introduces an interactive Memex-like application using a self-modifiable Petri Net model – Self-modifiable Color Petri Net (SCPN). The Memex (“memory extender”) device proposed by Vannevar Bush in 1945 focused on the problems of “locating relevant information in the published records and recording how that information is intellectually connected.” The important features of Memex include associative indexing and retrieval. In this paper, the self-modifiable functions of SCPN are used to achieve trail recording and retrieval. A place in SCPN represents a website and an arc indicates the trail direction. Each time when a new website is visited, a place corresponding to this website will be added. After a trail is built, users can use it to retrieve the websites they have visited. Besides, useful user interactions are supported by SCPN to achieve Memex functions. The types of user interactions include: forward, backward, history, search, etc. A simulator has been built to demonstrate that the SCPN model can realize Memex functions. Petri net instances can be designed to model trail record, back, and forward operations using this simulator. Furthermore, a client-server based application system has been built. Using this system, a user can surf online and record his surfing history on the server according to different topics and share them with other users

Exploiting the user interaction context for automatic task detection

Detecting the task a user is performing on her computer desktop is important for providing her with contextualized and personalized support. Some recent approaches propose to perform automatic user task detection by means of classifiers using captured user context data. In this paper we improve on that by using an ontology-based user interaction context model that can be automatically populated by (i) capturing simple user interaction events on the computer desktop and (ii) applying rule-based and information extraction mechanisms. We present evaluation results from a large user study we have carried out in a knowledge-intensive business environment, showing that our ontology-based approach provides new contextual features yielding good task detection performance. We also argue that good results can be achieved by training task classifiers `online' on user context data gathered in laboratory settings. Finally, we isolate a combination of contextual features that present a significantly better discriminative power than classical ones