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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Book ChapterDOI
Hybrid web recommender systems
TL;DR: This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies and finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths.
Journal ArticleDOI
Protein-to-protein interactions: Technologies, databases, and algorithms
TL;DR: The article reviews PPI data representation and storage, as well as PPI databases, and describes the main PPI models, mostly based on graphs, which are discussed in depth.
Journal ArticleDOI
A taxonomy of sequential pattern mining algorithms
TL;DR: This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application and attempts to provide a comparative performance analysis of many of the key techniques.
Book ChapterDOI
Recommender Systems in Technology Enhanced Learning
TL;DR: Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H. K., & Koper, R. (2011).
Journal ArticleDOI
A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition
Ana Belen Barragans-Martinez,Enrique Costa-Montenegro,Juan C. Burguillo,Marta Rey-López,Fernando A. Mikic-Fonte,Ana Peleteiro +5 more
TL;DR: The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc.