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

An Exploratory Study of Personalization and Learning Systems Continuance

Learning systems are widely adopted by institutions worldwide in the new millennium. The challenge on utilization of learning systems is switched from users’ pre-acceptance behaviours (whether they are likely to adopt learning systems) to post-acceptance behaviours (whether they will continue to use the learning systems). It is commonly expected that successfully adopted learning systems that have, at one time, been perceived as being useful and easy to use would likely achieve a high rate of user continuance. However, reality can be different as user continuance is often not as high as expected. The continuance of learning systems draws our attention because the investment in institutionalizing a learning system is huge. There is also a theoretical gap between technology acceptance and system continuance for which continuance behaviour cannot be explained by traditional technology acceptance models. This study extends a post-adoption model on habit and IS continuance to investigate the effect of personalization (which includes personal content management, personal time management and privacy control) on learning system continuance. Empirical results suggest that personalization has a positive influence on perceived usefulness and habit, but does not directly influence continuance intention

Uncovering perceived identification accuracy of in-vehicle biometric sensing

Biometric techniques can help make vehicles safer to drive, authenticate users, and provide personalized in-car experiences. However, it is unclear to what extent users are willing to trade their personal biometric data for such benefits. In this early work, we conducted an open card sorting study (N=11) to better understand how well users perceive their physical, behavioral and physiological features can personally identify them. Findings showed that on average participants clustere