scholarly journals Contextual Information Retrieval within Recommender System: Case Study "E-learning System"

TEM Journal ◽  
2020 ◽  
pp. 1150-1162
Author(s):  
Mourad Brik ◽  
Mohamed Touahria
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Research Work ◽  
User Profile ◽  
Learning Objects ◽  
Learning System ◽  
Context Information ◽  
Context Modeling ◽  
Semantic Web Technologies ◽  
Ubiquitous Environment

This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition…) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.

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.

Pre-Filling Collaborative Filtering Algorithm Based on Matrix Factorization

2013 ◽  
Vol 411-414 ◽  
pp. 2223-2228
Author(s):  
Dong Liang Su ◽  
Zhi Ming Cui ◽  
Jian Wu ◽  
Peng Peng Zhao
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Matrix Factorization ◽  
Nearest Neighbor ◽  
User Profile ◽  
Personalized Recommendation ◽  
System Quality ◽  
Recommendation Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Personalized Recommender System

Nowadays personalized recommendation algorithm of e-commerce can hardly meet the needs of users as an ever-increasing number of users and items in personalized recommender system has brought about sparsity of user-item rating matrix and the emergence of more and more new users has threatened recommender system quality. This paper puts forward a pre-filled collaborative filtering recommendation algorithm based on matrix factorization, pre-filling user-item matrixes by matrix factorization and building nearest-neighbor models according to new user profile information, thus mitigating the influence of matrix sparsity and new users and improving the accuracy of recommender system. The experimental results suggest that this algorithm is more precise and effective than the traditional one under the condition of extremely sparse user-item rating matrix.

scholarly journals THE RECOMMENDATION ALGORITHM FOR AN ONLINE ART GALLERY

2018 ◽  
Vol 7 (2) ◽  
pp. 108-119
Author(s):  
Waldemar Karwowski ◽  
Marian Rusek ◽  
Joanna Sosnowska
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation Systems ◽  
User Profile ◽  
Art Gallery ◽  
Recommendation Algorithm ◽  
Design And Implementation ◽  
Filtering Technique

The paper discusses the need for recommendations and the basic recommendation systems and algorithms. In the second part the design and implementation of the recommender system for online art gallery (photos, drawings, and paintings) is presented. The designed customized recommendation algorithm is based on collaborative filtering technique using the similarity between objects, improved by information from user profile. At the end conclusions of performed algorithm are formulated.

scholarly journals Comparative Study of Machine Learning Algorithms for Recommendation System

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Sonam Singh ◽  
◽  
Kriti Srivastva ◽  
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Research Work ◽  
Previous History ◽  
Machine Learning Algorithms ◽  
The Given ◽  
User History

The role of recommender system is very vital in recent times for a lot of individuals. It helps in taking decisions without exploring physically. Broadly there are two types of recommender system: Content based and Collaborative Filtering. The first one focus on user’s history and takes decisions. But there could be times when decisions based on only user history is not sufficient. For this, there is a need to analyze many parameters influencing the decision such as previous history, Age, gender, location etc. In the second approach it finds similar group of users based on several parameters and then takes decisions. Over the last few decades machine learning algorithms have proved their worth in this area because of their ability to learn from the given data and identify various hidden patterns. With this learning, these algorithms are able to generalize very well for unknown data. In this research work, a survey on three different machine learning based collaborative filtering methods are presented using Movie Lens dataset. The comparison of all three methods based on RMSE and MAE error is also discussed.

scholarly journals Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

2021 ◽  
Author(s):  
Ben Ashley
Keyword(s):  
Augmented Reality ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
User Profile ◽  
Cultural Research ◽  
Augmented Reality Application ◽  
Pros And Cons ◽  
Object Similarity ◽  
Content Based Filtering

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

scholarly journals Relevant Influence of Semantic Web Framework on Smart E-Learning Environment

2021 ◽  
Vol 16 (17) ◽  
pp. 177
Author(s):  
Subhabrata Sengupta ◽  
Anish Banerjee ◽  
Satyajit Chakrabarti
Keyword(s):  
Online Learning ◽  
Semantic Web ◽  
Learning Objects ◽  
Learning System ◽  
Semantic Web Technologies ◽  
Educational Management ◽  
Learning Framework ◽  
Self Organized ◽  
E Learning ◽  
Efficient Learning

E-Learning systems have unbound prospects to deliver unmatched effective learning services and feedback assistance than what it is presently offering through mediums like online tutoring, or other electronic educational management services. Different stages and application potentials of Semantic Web technology and it’s architecture can be applied at different sectors and phases of the E-Learning framework to amplify the quality and versatility of services. Features of Semantic Web have been explored in the sectors with respect to instructors to plan, analyse and execute their tasks and also in making a sustainable system that interprets the structure of distributed, self organized, and self-instructed online learning to monitor it’s influence on performance. The main objective of this work is to study how electronic and online learning frameworks can be improved and enhanced by the influence of semantic web technologies in understanding and simplifying concept clarification and description, reusable learning objects (LOs), and benefits of the applying ontology in describing the learning materials for a better and more efficient learning system.

Application of Fuzzy User's Profile for Mining Reusable E-Learning Repositories on Web through Lightweight Mobile Agent

2015 ◽  
pp. 500-521
Author(s):  
Priti Srinivas Sajja
Keyword(s):  
Mobile Agent ◽  
Research Work ◽  
User Profile ◽  
Learning System ◽  
Fuzzy Membership Function ◽  
Learning Material ◽  
Light Weight ◽  
Agent Based ◽  
E Learning ◽  
Generic Design

The creditability of an e-Learning system depends on its content, services and presentation of the material to the learners. Besides providing material on demand, an e-Learning system also manages knowledge for future use. It is observed that the learning material available on different locations may be reused in a proper way. The work presented here discusses generic design of an e-Learning system with various reusable learning material repositories. The architecture described here uses light weight mobile agents in order to access these repositories by taking help of fuzzy user profile. With notion of the fuzzy user profile, the system knows more about users' need and can present customized content to the users. Besides the architecture of the e-Learning system, the chapter also discusses the necessary concepts about the fuzzy logic and agent based systems, in depth literature survey, structure of the user profile, fuzzy membership function and design of the light weight mobile agent with necessary implementation details. At the end, the chapter concludes with the applications, advantages and future scope of the research work possible in the domain.

A Recommender System for Learning Objects Personalized Retrieval

2012 ◽  
pp. 182-210 ◽  
Author(s):  
Ana Casali ◽  
Valeria Gerling ◽  
Claudia Deco ◽  
Cristina Bender
Keyword(s):  
Recommender System ◽  
User Profile ◽  
Learning Objects ◽  
Educational Resources ◽  
Agent Architecture ◽  
Profile Data ◽  
Personalized Search ◽  
Content Based Retrieval ◽  
Multi Agent ◽  
The Subject

This chapter describes the development of a recommender system of learning objects. This system helps a user to find educational resources that are most appropriate to his/her needs and preferences. The search is performed in different repositories of learning objects, where each object has descriptive metadata. Metadata is used to retrieve objects that satisfy not only the subject of the query, but also the user profile, taking into account his/her characteristics and preferences. A multi-agent architecture that includes several types of agents with different functionalities is used. In this chapter, we describe the modelization of the Personalized Search Agent (PS-Agent) as a graded BDI (Belief-Desire-Intention) agent. This agent is responsible for making a flexible content-based retrieval and provides an ordered list of the resources that better meet the user profile data. A prototype was implemented, and experimentation results are presented.

scholarly journals Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

2021 ◽  
Author(s):  
Ben Ashley
Keyword(s):  
Augmented Reality ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
User Profile ◽  
Cultural Research ◽  
Augmented Reality Application ◽  
Pros And Cons ◽  
Object Similarity ◽  
Content Based Filtering

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

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