User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page

Relating personality types with user preferences in multiple entertainment domains

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) Late-Breaking Results, Project Papers and Workshop Proceedings of the 21st Conference on User Modeling, Adaptation, and Personalization, UMAP 2013We present a preliminary study on the relations between personality types and user preferences in multiple entertainment domains, namely movies, TV shows, music, and books. We analyze a total of 53,226 Facebook user profiles composed of both personality scores (openness, conscientiousness, extraversion, agreeableness, neuroticism) from the Five Factor model, and explicit interests about 16 genres in each of the above domains. As a result of our analysis, we extract personality-based user stereotypes and association rules for some of the considered domain genres, and infer similarities of personality types related to genres in different domains.This work was supported by the Spanish Government (TIN2011-28538-C02) and the Regional Government of Madrid (S2009TIC-1542). The authors sincerely thank the members of myPersonality project for their kind attention and help on downloading and processing the provided data

A Customized Artificial Intelligence Based Career Choice Recommender System for a Rural University

Rapid technological developments have enabled users to be supported and guided in decision-making. An example of this is the ability of tertiary students to use technology to explore different career options and make informed decisions about their future. Notwithstanding the increasing use of technology in general, the technology for career guidance and personalized career recommendations in South Africa is still limited. There are some limiting factors such as the ever-looming challenge of limited access to technology, language barriers and cultural differences that are prevalent in rural areas. With this premise, this study collected quantitative data from students at an Eastern Cape University in South Africa, in which the students participated on how they use artificial intelligence tools and technologies in their career choice process. The study highlighted the need for bespoke, locally developed job assessment systems that are more effective and culturally appropriate for a South African university student, particularly in rural areas. Participants would prefer to be engaged, be part of and propose their suggestions on the developed career choice, as current ones do not exactly refer to their context. Tailor made and customized career guidance solutions with Artificial Intelligence (AI) capabilities have more chances of adoption and usage by targeted user

Design of a recommender system for web based learning

The design of recommender systems is an ongoing research area where several researchers have devised means of incorporating intelligence in web content systems to be able to provide recommendations to learners on the basis of their learning preferences i.e. based on their learning profiles. The paper discusses the design of such a system based mapped to a content ontology and learner profiles created in the system

Personalised trails and learner profiling within e-learning environments

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Integrating multiple intelligences and personality traits in a dynamic personal decision aid for youth

As far as the development of youth community is concerned, the implementation of a dedicated decision aid is believed to have ample potentials in building their skills in making decisions.The absence of proper guidance in making crucial decisions could cause irreversible effects to youth’s future and consequently to the development plan of the country.Accordingly, this study focuses on the development of a computerized personal decision aid for youth named as Youth Personal Decision Aid (YouthPDA).The aid manifests the integration of Personality Traits (PT) and Multiple Intelligence (MI) data in a contextual aware recommender system.The system uses Rule Based Reasoning (RBR) that will display the recommendations based on set of programmed rules. This paper also discusses findings from helpfulness evaluation of YouthPDA, which comprises of four dimensions; reliability, decision-making effort, confidence, and decision process awareness. The mean value for each dimension (which is >5) indicated that the YouthPDA is accepted to be a helpful tool for youth in making decision