A Proposed Method to Recognize the Research Trends using Web-based Search Engines

Elkobaisi, Mohammed R.; Maatuk, Abdelsalam M.; Aljawarneh, Shadi

doi:10.1145/2832987.2833012

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…Y. Liu, Yang, Ma, Xu, and Hua () proposed a research project recommender system for undergraduate students, which benefited from the profiles of students as long as their social connections exist in the academic collaboration network. Elkobaisi, Maatuk, and Aljawarneh () proposed the extraction of trending topics in search engines to retrieve interesting information that can be used later by recommender systems and other applications. Rivolli, Soares, and de Carvalho () proposed a multilabel method for food truck recommendation.…”

Section: Related Workmentioning

confidence: 99%

An efficient hybrid similarity measure based on user interests for recommender systems

et al. 2019

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Recommender systems are used to suggest items to users based on their interests. They have been used widely in various domains, including online stores, web advertisements, and social networks. As part of their process, recommender systems use a set of similarity measurements that would assist in finding interesting items. Although many similarity measurements have been proposed in the literature, they have not concentrated on actual user interests. This paper proposes a new efficient hybrid similarity measure for recommender systems based on user interests. This similarity measure is a combination of two novel base similarity measurements: the user interest-user interest similarity measure and the user interest-item similarity measure. This hybrid similarity measure improves the existing work in three aspects. First, it improves the current recommender systems by using actual user interests. Second, it provides a comprehensive evaluation of an efficient solution to the cold start problem. Third, this similarity measure works well even when no corated items exist between two users. Our experiments show that our proposed similarity measure is efficient in terms of accuracy, execution time, and applicability. Specifically, our proposed similarity measure achieves a mean absolute error (MAE) as low as 0.42, with 64% applicability and an execution time as low as 0.03 s, whereas the existing similarity measures from the literature achieve an MAE of 0.88 at their best; these results demonstrate the superiority of our proposed similarity measure in terms of accuracy, as well as having a high applicability percentage and a very short execution time.

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Section: Related Workmentioning

confidence: 99%

An efficient hybrid similarity measure based on user interests for recommender systems

et al. 2019

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“…Machine learning and information security techniques (Radhakrishna et al 2017;Aljawarneh et al 2016a, b;Aljawarneh et al 2007;Radhakrishna et al 2016Radhakrishna et al , 2018Aljawarneh 2017;Kalpana et al 2016;Elkobaisi et al 2015;Aljawarneh et al 2017) and approaches have been used in assessment for e-learning to improve the learning quality for higher education.…”

Section: Introduction To the Domain Of Researchmentioning

confidence: 99%

Special issue on the current trends in E-learning Assessment

2019

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In this full review paper, the recent emerging trends in E-learning Assessment have been reviewed and explored to address the recent topics and contributions in the era of Distance Education. This includes a set of rigorously reviewed world-class manuscripts addressing and detailing state-of-the-art, frameworks and techniques research projects in the area of E-learning Assessment, using different approaches such as Blockchain, Gamification, Process Mining, among others. Based on this systematic review, we have put some recommendations and suggestions for researchers, practitioners and scholars to improve their research quality in this area.

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Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

2022

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Emotions are an essential part of a person’s mental state and influence her/his behavior accordingly. Consequently, emotion recognition and assessment can play an important role in supporting people with ambient assistance systems or clinical treatments. Automation of human emotion recognition and emotion-aware recommender systems are therefore increasingly being researched. In this paper, we first consider the essential aspects of human emotional functioning from the perspective of cognitive psychology and, based on this, we analyze the state of the art in the whole field of work and research to which automated emotion recognition belongs. In this way, we want to complement the already published surveys, which usually refer to only one aspect, with an overall overview of the languages ontologies, datasets, and systems/interfaces to be found in this area. We briefly introduce each of these subsections and discuss related approaches regarding methodology, technology, and publicly accessible artefacts. This comes with an update to recent findings that could not yet be taken into account in previous surveys. The paper is based on an extensive literature search and analysis, in which we also made a particular effort to locate relevant surveys and reviews. The paper closes with a summary of the results and an outlook on open research questions.

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A Proposed Method to Recognize the Research Trends using Web-based Search Engines

Cited by 14 publications

References 6 publications

An efficient hybrid similarity measure based on user interests for recommender systems

An efficient hybrid similarity measure based on user interests for recommender systems

Special issue on the current trends in E-learning Assessment

Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

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