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.

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.

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 Personalized Recommender System for Calculus using Content-Based Filtering Approach

2018 ◽  
Vol 7 (3.15) ◽  
pp. 110 ◽  
Author(s):  
Noor Latiffah Adam ◽  
Muhammad Alif Zulkafli ◽  
Shaharuddin Cik Soh ◽  
Nor Ashikin Mohamad Kamal ◽  
Nordin Abu Bakar
Keyword(s):  
Recommender System ◽  
The Internet ◽  
Internet Users ◽  
Basic Calculus ◽  
Personalized Recommender System ◽  
Many Sources ◽  
Content Based Filtering ◽  
Human Kind ◽  
The Web ◽  
Filtering Approach

In this millennial age, Internet is becoming essential to human kind. Along with the growth of Internet users, information is also becoming huge and starting to cause difficulties to find the relevant contents. Thus, the recommender system was introduced. It helps the user to suggest the items based on the user’s preferences. This system could help the students as Calculus is one of the tough subjects feared by most students. Credits given to the technology as many sources on the web can provide tutorials, working examples and solutions on the subjects. However, there are too many of them. Students had to make a few selections, which one can fulfil their needs of specific calculus topics. The personalized recommender system developed was a content-based filtering recommender system with its own scraping engine to collect the sources from the Internet which focuses on the basic Calculus topics. The system and engine were constructed by using Flask framework together with its relevant libraries. 

scholarly journals Measuring learner's performance in e-learning recommender systems

2010 ◽  
Vol 26 (6) ◽  
Author(s):  
Khairil Imran Ghauth ◽  
Nor Aniza Abdullah
Keyword(s):  
Learning Outcomes ◽  
Recommender Systems ◽  
Recommender System ◽  
Knowledge Gain ◽  
Online Systems ◽  
Post Test ◽  
Score Gain ◽  
E Learning ◽  
Student’S Performance ◽  
Content Based Filtering

<span>A recommender system is a piece of software that helps users to identify the most interesting and relevant learning items from a large number of items. Recommender systems may be based on collaborative filtering (by user ratings), content-based filtering (by keywords), and hybrid filtering (by both collaborative and content-based filtering). Recommender systems have been a useful tool to recommend items in many online systems, including e-learning. However, not much research has been done to measure the learning outcomes of the learners when they use e-learning with a recommender system. Instead, most of the researchers were focusing on the accuracy of the recommender system in predicting the recommendation rather than the knowledge gain by the learners. This research aims to compare the learning outcomes of the learners when they use several types of e-learning recommender systems. Based on the comparison made, we propose a new e-learning recommender system framework that uses content-based filtering and good learners' ratings to recommend learning materials, and in turn is able to increase the student's performance. The results show that students who used the proposed e-learning recommender system produced a significantly better result in the post-test. The results also show that the proposed e-learning recommender system has the highest percentage of score gain from pre-test to post-test.</span>

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

scholarly journals Crowdsourced Recommender System

2018 ◽  
Author(s):  
Mario Mallia Milanes ◽  
Matthew Montebello
Keyword(s):  
Web 2.0 ◽  
Recommender Systems ◽  
Recommender System ◽  
Information Overload ◽  
Relevant Information ◽  
Learning Systems ◽  
Learning Material ◽  
Timely Manner ◽  
Online Learners ◽  
E Learning

The use of artificially intelligent techniques to overcome specific shortcomings within e-learning systems is a well-researched area that keeps on evolving in an attempt to optimise such resourceful practices. The lack of personalization and the sentiment of isolation coupled with a feeling of being treated like all others, tends to discourage and push learners away from courses that are very well prepared academically and excellently projected intellectually. The use of recommender systems to deliver relevant information in a timely manner that is specifically differentiated to a unique learner is once more being investigated to alievate the e-learning issue of being impersonal.  The application of such a technique also assists the learner by reducing information overload and providing learning material that can be shared, criticized and reviewed at one’s own pace. In this paper we propose the use of a fully automated recommender system based on recent AI developments together with Web 2.0 applications and socially networked technologies. We argue that such technologies have provided the extra capabilities that were required to deliver a realistic and practical interfacing medium to assist online learners and take recommender systems to the next level.

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