Journal ArticleDOI
A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining
TLDR
The proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.About:
This article is published in Future Generation Computer Systems.The article was published on 2017-07-01. It has received 195 citations till now. The article focuses on the topics: Recommender system & Domain knowledge.read more
Citations
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Journal ArticleDOI
A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques
TL;DR: This research solves two main drawbacks of recommender systems, sparsity and scalability, using dimensionality reduction and ontology techniques, and uses ontology to improve the accuracy of recommendations in CF part.
Journal ArticleDOI
Review of ontology-based recommender systems in e-learning
Gina George,Anisha M. Lal +1 more
TL;DR: The comprehensive survey in this paper gives an overview of the research in progress using ontology to achieve personalization in recommender systems in the e-learning domain.
Journal ArticleDOI
EARS: Emotion-aware recommender system based on hybrid information fusion
TL;DR: An emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features is proposed.
Journal ArticleDOI
A hybrid recommender system for e-learning based on context awareness and sequential pattern mining
TL;DR: Evaluation of the proposed hybrid recommendation approach combining context awareness, sequential pattern mining (SPM) and CF algorithms for recommending learning resources to the learners indicated that it can outperform other recommendation methods in terms of quality and accuracy of recommendations.
Journal ArticleDOI
A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields
TL;DR: It was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field.
References
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Journal ArticleDOI
A hybrid collaborative filtering recommendation mechanism for P2P networks
TL;DR: A P2P-based hybrid collaborative filtering mechanism for the support of combining user-based and item attribute-based ratings is considered and a Hybrid collaborative filtering (HCF) algorithm is presented to improve the predictive accuracy.
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A Hybrid Trust-Based Recommender System for Online Communities of Practice
TL;DR: This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs and finds that the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm.
Journal ArticleDOI
A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback
TL;DR: This paper is first academic systematic literature review of CF technique along with implicit data from user behaviors and activities to aggregate existing evidence as a synthesis of best quality scientific studies.
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An Effective Recommendation Framework for Personal Learning Environments Using a Learner Preference Tree and a GA
TL;DR: This paper models the dynamic multipreferences of learners using the multidimensional attributes of resource and learner ratings by using data mining technology to alleviate sparsity and cold-start problems and increase the diversity of the recommendation list.
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An efficient approach for finding weighted sequential patterns from sequence databases
TL;DR: An efficient approach for finding weighted sequential patterns from sequence databases is proposed and a tightening strategy in the proposed approach is proposed to obtain more accurate weighted upper-bounds for subsequences in mining.