Course Recommendation Model in Academic Social Networks Based on Association Rules and Multi -similarity

Huang, Xianyu; Tang, Yong; Qu, Rong; Li, Chunying; Yuan, Chengzhe; Sun, Saimei; Xu, Bixia

doi:10.1109/cscwd.2018.8465266

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…(1) College students were assisted in selecting electives by combining a multi-criteria hybrid recommendation system that utilizes CF and CBF with genetic optimization, which was introduced by authors in [40], (2) Authors in [41] overcame the performance implications of traditional algorithms by presenting a hybrid approach that includes using association rule-mining and collaborative filtering, (3) Authors in [42] used data mining and recommendation systems to assist academic advisers and students in creating effective study plans, particularly when a student has failed a few courses, (4) Authors in [43] overcame the cold-start drawback of collaborative filtering and the domain knowledge requirement of content-based filtering by using a hybrid approach that combines both, (5) Authors in [44] represented the student learning styles and the learning object profiles using the Felder-Silverman learning styles model, thus improving the overall accuracy of recommendations, (6) Authors in [45] suggested a Course Recommendation Model in Academic Social Networks Based on Association Rules and Multi-similarity (CRM-ARMS) that is based on academic social networks, a hybrid approach combining an association rules algorithm and an improved multi-similarity algorithm of multi-source information, which can recommend courses by possible relationships between courses and the user's implicit interests.…”

Section: Aim Of Studies That Used Content-based Filteringmentioning

confidence: 99%

“…It is noticeable that most research papers utilizing this approach used datasets with a very small number of students. Five research papers used private datasets and only [45] used a public dataset. Most research studies utilizing this approach have not provided any information on any preprocessing steps performed on the dataset.…”

Section: Dataset Description Of Studies That Used Content-based Filte...mentioning

confidence: 99%

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Systematic Review of Recommendation Systems for Course Selection

Algarni

Sheldon

2023

MAKE

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Course recommender systems play an increasingly pivotal role in the educational landscape, driving personalization and informed decision-making for students. However, these systems face significant challenges, including managing a large and dynamic decision space and addressing the cold start problem for new students. This article endeavors to provide a comprehensive review and background to fully understand recent research on course recommender systems and their impact on learning. We present a detailed summary of empirical data supporting the use of these systems in educational strategic planning. We examined case studies conducted over the previous six years (2017–2022), with a focus on 35 key studies selected from 1938 academic papers found using the CADIMA tool. This systematic literature review (SLR) assesses various recommender system methodologies used to suggest course selection tracks, aiming to determine the most effective evidence-based approach.

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Section: Aim Of Studies That Used Content-based Filteringmentioning

confidence: 99%

Section: Dataset Description Of Studies That Used Content-based Filte...mentioning

confidence: 99%

Systematic Review of Recommendation Systems for Course Selection

Algarni

Sheldon

2023

MAKE

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“…[2]. ASNs provide various research topics, such as community detection [16,17], knowledge sharing [18,19], and recommendation systems [20,21]. A line of research focuses on making use of ASNs for particular recommendations [21].…”

Section: Academic Social Networkmentioning

confidence: 99%

“…ASNs provide various research topics, such as community detection [16,17], knowledge sharing [18,19], and recommendation systems [20,21]. A line of research focuses on making use of ASNs for particular recommendations [21]. A course recommendation model in ASNs was proposed, which combines association rules algorithm with an improved multi-similarity algorithm of multi-source information.…”

Section: Academic Social Networkmentioning

confidence: 99%

Graph Embedding for Scholar Recommendation in Academic Social Networks

Yuan

Lin

et al. 2021

Front. Phys.

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The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates) and various relationships (e.g., collaboration relationship between team members, citations, and co-authorships), which forms a complex heterogeneous academic network. Our goal is to recommend potential similar scholars for users in ASNs. In this article, we propose to design a graph embedding-based scholar recommendation system by leveraging academic auxiliary information. First, we construct enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by a graph embedding framework, which helps the system measure the similarity between scholars based on their representation vectors. Finally, the system generates potential similar scholars for users in ASNs for the final recommendation. We evaluate the effectiveness of our model on five real-world datasets: SCHOLAT, Zhihu, APS, Yelp and Gowalla. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.

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“…Yin et al (2020) introduced a recommendation model that used transition probability based on the learners' enrolment history. They calculated the percentage of learners who take course A after attending Additionally, they used the semantic structure of course topics and their connection to strengthen their hybrid recommendation model Huang et al (2018). used an FP-growth association rule mining algorithm as part of the proposed hybrid recommendation system to find the relation between courses.…”

mentioning

confidence: 99%

Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review

Narimani,

Barberà

2024

IRRODL

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As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.

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Course Recommendation Model in Academic Social Networks Based on Association Rules and Multi -similarity

Cited by 9 publications

References 3 publications

Systematic Review of Recommendation Systems for Course Selection

Systematic Review of Recommendation Systems for Course Selection

Graph Embedding for Scholar Recommendation in Academic Social Networks

Extracting Course Features and Learner Profiling for Course Recommendation Systems: A Comprehensive Literature Review

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