scholarly journals A Review of Content-Based and Context-Based Recommendation Systems

2021 ◽  
Vol 16 (03) ◽  
pp. 274
Author(s):  
Umair Javed ◽  
Kamran Shaukat ◽  
Ibrahim A. Hameed ◽  
Farhat Iqbal ◽  
Talha Mahboob Alam ◽  
...  
Keyword(s):  
Recommender System ◽  
Recommendation Systems ◽  
Context Aware ◽  
Semantic Approach ◽  
Web Based ◽  
Ontology Language ◽  
E Learning ◽  
Description Framework ◽  
Hybrid Recommender ◽  
Study Context

In our work, we have presented two widely used recommendation systems. We have presented a context-aware recommender system to filter the items associated with user’s interests coupled with a context-based recommender system to prescribe those items. In this study, context-aware recommender systems perceive the user’s location, time, and company. The context-based recommender system retrieves patterns from World Wide Web-based on the user’s past interactions and provides future news recommendations. We have presented different techniques to support media recommendations for smartphones, to create a framework for context-aware, to filter E-learning content, and to deliver convenient news to the user. To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL). We have also used the Resource Description Framework (RDF), JAVA, machine learning, semantic mapping rules, and natural ontology languages that suggest user items related to the search. In our work, we have used E-paper to provide users with the required news. After applying the semantic reasoning approach, we have concluded that by some means, this approach works similarly as a content-based recommender system since by taking the gain of a semantic approach, we can also recommend items according to the user’s interests. In a content-based recommender system, the system provides additional options or results that rely on the user’s ratings, appraisals, and interests.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

Context-Aware Ontological Hybrid Recommender System For IPTV

2018 ◽  
Author(s):  
Mohammad Wahiduzzaman Khan ◽  
Gaik-Yee Chain ◽  
Fang-Fang Chua ◽  
Su-Cheng Haw ◽  
Muhsin Hassan ◽  
...  
Keyword(s):  
Recommender System ◽  
Context Aware ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

2019 ◽  
Vol 15 (2) ◽  
pp. 85-107
Author(s):  
Zameer Gulzar ◽  
L. Arun Raj ◽  
A. Anny Leema
Keyword(s):  
Recommender System ◽  
Hybrid Approach ◽  
Learning Systems ◽  
E Learning ◽  
Hybrid Recommender System ◽  
The Individual ◽  
Hybrid Recommender ◽  
Nearest Neighborhood ◽  
Personal Interests ◽  
Hybrid Methodology

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.

Automatic Personalization in E-Learning Based on Recommendation Systems

2012 ◽  
pp. 19-33 ◽  
Author(s):  
Mohamed Koutheaïr Khribi ◽  
Mohamed Jemni ◽  
Olfa Nasraoui
Keyword(s):  
Large Scale ◽  
Web Mining ◽  
Recommendation Systems ◽  
Educational Content ◽  
Web Based ◽  
Web Based Learning ◽  
Web Objects ◽  
Navigation Support ◽  
E Learning ◽  
Dramatic Growth

Web based learning environments are being increasingly used at a large scale in the education area. This situation has brought a dramatic growth in the amount of educational resources and services incorporated continuously in these systems, and related access and usage of this educational content by a diversity of learners. However, the delivery of this educational content is generally done in the same way for all learners without giving any special attention to the different consumption styles or differences between their profiles and individual needs. Therefore, providing personalization in e-learning systems has to be considered as a necessity and not an option. Recommending suitable links represents an instance of adaptive navigation support technology. E-learning recommender systems are used to locate relevant educational Web objects that better match the learner’s profile and interests, this requires the ability of a system to predict learner’s needs and preferences. Therefore, recommendation systems need to use Web mining techniques in one or more phases of the recommendation process, especially in the modelling and pattern discovery phase. Most emergent recommendation systems in e-learning tend to rely on automated detection of student’s preferences and needs since it is more efficient and attractive to provide needed support to students without requesting any explicit information from them. In this chapter, we present an overview of personalization in e-learning based on recommendation systems and Web mining techniques.

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