Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Tarus, John K.; Niu, Zhendong; Mustafa, Ghulam

doi:10.1007/s10462-017-9539-5

Cited by 387 publications

(212 citation statements)

References 89 publications

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“…Additionally, from the perspective of meteorological teaching, how to organize knowledge efficiently to establish accurate push for professionals and students in this field is worthy of further study (Tarus et al , ; Wan & Niu, ). Similarly, how to mine new knowledge in the measurement of scientific literature, and even predict new hotspots is also an important direction of meteorological knowledge services (Tarus et al , ; Nie et al , ; Yousif et al , ).…”

Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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Section: Discussion: Challenges and Future Of Social Weathermentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

Self Cite

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“…To serve the new researchers in getting a diagram of the research performed in a specific zone, authors have proposed keywords based retrieval procedure in [12] for giving an overview and a various arrangement of papers as a piece of the preliminary reading list. A literature review is presented on ontology-based recommender frameworks in the domain of elearning [13]. This investigation demonstrates that intersection of information based proposal with other suggestion methods can upgrade the viability of e-learning recommender systems.…”

Section: Literature Reviewmentioning

confidence: 94%

Recommender System for Journal Articles using Opinion Mining and Semantics

Sardar¹,

Ferzund²,

Suryani³

et al. 2017

ijacsa

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Abstract-Till date, the dominant part of Recommender Systems (RS) work focusing on single domain, i.e. for films, books and shopping and so on. However, human inclinations may traverse over numerous areas. Thus, utilization practices on related things from various domains can be valuable for RS to make recommendations. Academic articles, such as research papers are the way to express ideas and thoughts for the research community. However, there have been a lot of journals available which recognize these technical writings. In addition, journal selection procedure should consider user experience about the journals in order to recommend users most relevant journal. In this work of journal recommendation system, the data about the user experience targeting various aspects of journals has been gathered which addresses user experience about any journal. In addition, data set of archive articles has been developed considering the user experience in this regard. Moreover, the user experience and gathered data of archives are analyzed using two different frameworks based on semantics in order to have better consolidated recommendations. Before submission, we offer services on behalf of the research community that exploit user reviews and relevant data to suggest suitable journal according to the needs of the author.

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“…Collaborative filtering (CF) has another rationale. The list of recommended items derives from the analysis of preferences of other people with interests similar to those of the user [3,4,6,9,10,[13][14][15]. Preferences in the community of users with similar interests constitute the single reference point in CF.…”

Section: Collaborativementioning

confidence: 99%

Detection of Opinion Communities with the Help of Chance-Corrected Measures of Agreement

Олейник

2020

SN COMPUT. SCI.

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This paper discusses the feasibility and benefits of incorporating coefficients of inter-coder agreement (Krippendorff's α, Bennett, Alpert and Goldstein's S, Scott's π and Cohen's κ) into recommender systems. It is argued that with their help it is possible to increase the accuracy of users' assessment of various items (texts, but also potentially images, movies, music and goods). Chance-corrected measures of similarity also allow for the detection of similarly minded users in a more accurate manner. Results of small-scale empirical tests inform the discussion. Predictions made using chance-corrected measures of similarity are compared with those based on more conventional measures of similarity, the cosine coefficient and the Jaccard index.

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Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Cited by 387 publications

References 89 publications

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Recommender System for Journal Articles using Opinion Mining and Semantics

Detection of Opinion Communities with the Help of Chance-Corrected Measures of Agreement

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