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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
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
Building user interest profiles from wikipedia clusters
Users of search systems are often reluctant to explicitly build profiles to indicate their search interests. Thus automatically building user profiles is an important research area for personalized search. One difficult component of doing this is accessing a knowledge system which provides broad coverage of user search interests. In this work, we describe a
method to build category id based user profiles from a user's
historical search data. Our approach makes significant use
of Wikipedia as an external knowledge resource
Personalized Course Sequence Recommendations
Given the variability in student learning it is becoming increasingly
important to tailor courses as well as course sequences to student needs. This
paper presents a systematic methodology for offering personalized course
sequence recommendations to students. First, a forward-search
backward-induction algorithm is developed that can optimally select course
sequences to decrease the time required for a student to graduate. The
algorithm accounts for prerequisite requirements (typically present in higher
level education) and course availability. Second, using the tools of
multi-armed bandits, an algorithm is developed that can optimally recommend a
course sequence that both reduces the time to graduate while also increasing
the overall GPA of the student. The algorithm dynamically learns how students
with different contextual backgrounds perform for given course sequences and
then recommends an optimal course sequence for new students. Using real-world
student data from the UCLA Mechanical and Aerospace Engineering department, we
illustrate how the proposed algorithms outperform other methods that do not
include student contextual information when making course sequence
recommendations
Personalized Emphasis Framing for Persuasive Message Generation
In this paper, we present a study on personalized emphasis framing which can
be used to tailor the content of a message to enhance its appeal to different
individuals. With this framework, we directly model content selection decisions
based on a set of psychologically-motivated domain-independent personal traits
including personality (e.g., extraversion and conscientiousness) and basic
human values (e.g., self-transcendence and hedonism). We also demonstrate how
the analysis results can be used in automated personalized content selection
for persuasive message generation
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
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