User Modeling and Profiling in Information Systems

Abstract

User modeling or user profiling is fundamental to manage information overload issues in many adaptive and personalized systems (e.g., recommender systems, personalized search engines, adaptive user interfaces). Although there are some literature review papers that provide an overview of existing studies in user modeling and their usage, there is currently a lack of bibliometric studies that can provide a systematic and quantitative overview of this research area. Therefore, this paper aims to complete the existing literature in this research area through a bibliometric study based on 52,027 relevant publications extracted from Scopus, a world-leading publisher-independent global citation database. The analyses enabled us to identify the most relevant publications, sources of publications, authors, institutions, countries, and their collaboration. We also identify and classify the twelve most important associated topics, along with their subtopics and their trends. Some identified weak signals in topic trend analysis also provide good ideas of potential future research directions.

The Effect of Facial Resemblance on Cooperative Behavior in the Sharing Economy

“…For platforms, since users frequently encounter and evaluate new partners, the results of this study suggest that a matching system may consider the match of personalities and that facial resemblance serves as means to approximate personality matching. A recommendation system can be enhanced by considering facial resemblance along with other criteria, provided that the personal photos of both parties are available (Tchuente, 2022). This research also suggests that the moderation effect of the transactional and social value of a transaction is greater for female users.…”

“…The contributions of this study are multifold. First, for practitioners, the findings of this study suggest personal photos might be used in the recommendation systems in the sharing economy platforms to facilitate cooperation by matching partners according to facial resemblance (Tchuente, 2022). Second, the authors propose and empirically verify the mediating process from facial resemblance to cooperative intention.…”

One decade of big data for firms' competitiveness: insights and a conceptual model from bibliometrics

“…The bibliometric analysis is particularly convenient for exploring and analyzing large volume of scientific data. The use of bibliometrics is gradually extending to all disciplines and is particularly suitable for performance analysis and science mapping at a time when the emphasis on empirical contributions is producing voluminous, fragmented and controversial research streams (Aria and Cuccurullo, 2017;Tchuente, 2022;Tchuente et al, 2021). This is the case in the context of this study as we found more than 1,816 relevant articles related to big data and firms' competitiveness from diverse subjects' areas, with a fast growth (see Figure 2).…”

Application of Big Data Technology in Enterprise Information Security Management and Risk Assessment

“…Second, it is necessary to be able to conduct RA based on big data, integrate and mine the database, carry out model analysis, and realize the RA grading of data. Next, it needs to implement the risk warning, prediction, and control of big data, organically combine the safety science theory and big data theory, and predict the trend of accidents that may be formed in the future, thereby eliminating or controlling the safety risks in a targeted way and giving scientific data support for risk management decision (Aryal et al, 2020;Tchuente, 2022). An enterprise information system's security RA process can be roughly divided into three stages: plan preparation, field evaluation, and analysis report.…”