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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Proceedings ArticleDOI
Research on the Application of Curriculum Knowledge Point Recommendation Algorithm Based on Learning Diagnosis Model
Baiqiang Gan,Chi Zhang +1 more
TL;DR: The experimental results show that the effectiveness and accuracy of the recommendation algorithm model proposed in this paper can meet the learning needs of learners.
Proceedings ArticleDOI
A Pragmatic Review on Different Approaches Used in E-Learning Recommender Systems
Athira S Nath,Selvam E +1 more
TL;DR: This paper reviews the various challenges as well as techniques used in an e-learning recommender system and suggests some intelligent methods in to the recommendation approach.
Journal ArticleDOI
Content Prioritization Based on Usage Pattern Analysis
Jonghwan Park,Younghoon Lee +1 more
TL;DR: To provide effective user manuals, this work calculated the vector representation of each element of the usage pattern and adopted a heterogeneous embedding approach, and trained InfoGAN (a generative adversarial network) to predict the usage of the user manual and prioritized and re-organized its content accordingly.
Book ChapterDOI
A Comprehensive Survey on Web Recommendations Systems with Special Focus on Filtering Techniques and Usage of Machine Learning
K. N. Asha,R. Rajkumar +1 more
TL;DR: This survey presents a comparative study among different types of recommender system based on various parameters and filtering schemes and shows a significant improvement inRecommender system by using machine learning based approaches.
References
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Book
Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Proceedings ArticleDOI
Item-based collaborative filtering recommendation algorithms
TL;DR: This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Journal ArticleDOI
Evaluating collaborative filtering recommender systems
TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Journal Article
Industry Report: Amazon.com Recommendations: Item-to-Item Collaborative Filtering.
TL;DR: This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.