Abstract
Finding information from a large collection of resources is a tedious and time-consuming process. Due to information overload, searchers often need help and assistance to search and find the information. Recommender system is one of the innovative solutions to the problem related to information searching and retrieval. It helps and assist searchers by recommending the possible solution based on the previous search activities. These activities can be obtained from the web log, which requires a web log mining approach to extract all the keywords. In this study, keywords obtained from the library web log were analysed and the search keyword patterns were obtained. These keyword patterns were from several databases or resources that were subscribed by the library. The finding revealed some of the popular keywords and the most searchable databases among the searchers. This information was used to design and develop the recommender system that can be used to assist other searchers. The usability test of the recommender system showed that it is beneficial and useful to the searchers. These findings will also benefit the management in planning and managing the subscription of online databases at the university’s library.
Highlights
Keystroke dynamics (KD) is a method of identifying users based on how the operator uses a keyboard to type [1, 2]
It can be concluded that the Malays VS Chinese had the highest average accuracy rate of 86.02% for the 50% learning ratio
The most distinguishable typing method was for the North versus Central and North versus East categories due to at 50% Support Vector Machine (SVM) learning, the accuracy obtained was over 80%
Summary
Keystroke dynamics (KD) is a method of identifying users based on how the operator uses a keyboard to type [1, 2]. Other researchers continue to conduct study using the soft biometric elements available to each individual using the elements such as mole, iris or eye retina [8], scar effect / tattoo [9], body figure [9], gender [10, 11], [12], ethnicity [13], eye color, height [12], weight, hair color [14], age, BMI, walking style [15], sitting style [16], eyebrow, blood type [17], heartbeat, talking style [18], vein image [19], facial shape [20], facial skin / figure, and ear shape [17]. This study incorporates soft biometric elements (culture, gender, region of birth and CGPA) in the KD study
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