Community Collaborative Filtering for E-Learning

2008 ◽  
Author(s):  
Jian Hu ◽  
Wei Zhang
Keyword(s):  
Collaborative Filtering ◽  
E Learning ◽  
Community Collaborative

Goal-Based Framework for Multi-User Personalized Similarities in e-Learning Scenarios

2014 ◽  
Vol 4 (1) ◽  
pp. 1-14 ◽  
Author(s):  
M. Waseem Chughtai ◽  
Imran Ghani ◽  
Ali Selamat ◽  
Seung Ryul Jeong
Keyword(s):  
Collaborative Filtering ◽  
Cold Start ◽  
Traditional Education ◽  
Experimental Result ◽  
Personalized Recommendation ◽  
Web Based ◽  
Web Based Learning ◽  
E Learning ◽  
Learning Scenarios ◽  
Content Based Filtering

Web-based learning or e-Learning in contrast to traditional education systems offer a lot of benefits. This article presents the Goal-based Framework for providing personalized similarities between multi users profile preferences in formal e-Learning scenarios. It consists of two main approaches: content-based filtering and collaborative filtering. Because only traditional content-based filtering is not sufficient to generate the recommendations for new-users, therefore, the proposed work hybridized multi user's collaborative filtering functionalities with personalized content-based profile preferences filtering. The main purpose of this proposed work is to (a) overcome the user-based cold-start profile recommendations and (b) improve the recommendations accuracy for new-users in formal e-learning recommendation systems. The experimental has been done by using the famous ‘MovieLens' dataset with 15.86% density of the user-item matrix with respect to ratings, while the evaluation of experimental results have been performed with precision mean and recall mean to test the effectiveness of Goal-based personalized recommendation framework. The Experimental result Precision: 81.90% and Recall: 86.56% show that the proposed framework goals performed well for the improvement of user-based cold-start issue as well as for content-based profile recommendations, using multi users personalized collaborative similarities, in formal e-Learning scenarios effectively.

Providing Personalized Services to Users in a Recommender System

2015 ◽  
Vol 10 (2) ◽  
pp. 26-48 ◽  
Author(s):  
Olukunle Oduwobi ◽  
Bolanle Adefowoke Ojokoh
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Learning Ability ◽  
Learning Material ◽  
Electronic Library ◽  
Learning Materials ◽  
Personalized Services ◽  
E Learning ◽  
Personalized Recommender System ◽  
Content Based Filtering

Instructors recommend learning materials to a class of students not minding the learning ability and reading habit of each student. Learners are finding it problematic to make a decision about which available learning materials best meet their situation and will be beneficial to their course of study. In order to address this challenge, a new e-learning material recommender system that is able to recommend quality items to learners individually is required. The aim of this work is to develop a Personalized Recommender System that switches between Content-based and Collaborative filtering techniques, with an objective to design an algorithm to recommend electronic library materials, as well as personalize recommendations to both new and existing users. Experiments were conducted with evaluations showing that the recommender system was most effective when content-based filtering and collaborative filtering were used to recommend items for new users and existing users respectively, and still achieve personalization.

Enhancing e-Learning Environment with Embedded Recommender Systems

2013 ◽  
pp. 234-253
Author(s):  
Mohamed S. El Sayed ◽  
Mona Nasr ◽  
Torky I. Sultan
Keyword(s):  
Learning Environment ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Learning Objects ◽  
E Learning ◽  
Content Based Filtering

Recommended learning objects are obtained by using a range of recommendation strategies based mainly on content based filtering and collaborative filtering approaches, each applied separately or in combination.

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