Recommendation System: State of the Art Approach

Aamir, Mohammad; Bhusry, Mamta

doi:10.5120/21281-4200

Cited by 23 publications

(12 citation statements)

References 24 publications

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“…Machine learning and information retrieval techniques are used to create a recommender system and to suggest potential items to the learner who are in need of these items. The modern Learning Management Systems are still offer the learning objects in indistinguishable way to the learners who posses various learning styles [3].…”

Section: Intoructionmentioning

confidence: 99%

Adaptation using Machine Learning for Personalized eLearning Environment based on Students Preference

Martin¹,

Maria²

2019

IJITEE

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Information gathering is a lifelong process of human being and the learning takes place from womb to tomb. Learners acquire, recognize, map the gathered information to knowledge and use it in day to day life. Advancement in ICT and the utilization of ICT in teaching and learning process has contributed an exponential growth. An eLearning solution has almost reached maturity, where the teaching and learning community have the proper digital infrastructure, smart phones, tablet computers and the best software platform. An innovation in teaching and learning sector has become an integral part and is mandatory. Hence the challenge is to suggest quality and appropriate learning materials to the learners. The research aims to categorize the learner according to their learning ability and to find the learning path to facilitate the learner to have appropriate and quality learning objects with the help machine learning techniques. The focus of this work is to come up with a system architecture which predicts and adapts the learner style, find the learning path and to provide the suitable learning objects in eLearning environment based on their preferences. Personalization will assist learner to improve their learning performance.

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Section: Intoructionmentioning

confidence: 99%

Adaptation using Machine Learning for Personalized eLearning Environment based on Students Preference

Martin¹,

Maria²

2019

IJITEE

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“…Technology Enhanced learning is the application of information and communication technologies to teaching and learning (Kirkwood and Price 2014). Recommendation Systems (RS) are software tools based on machine learning and information retrieval techniques (Aamir and Bhusry, 2015) that provide suggestions for potential useful items to someone's interest (Ricci et al, 2011). RSs' are widely used in many fields including TEL (Verbert et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

A Personalized Learning Recommendation System Architecture for Learning Management System

Syed

Palade

Iqbal

et al. 2017

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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The information on the web is ever increasing and it is becoming difficult for students to find appropriate information or relevant learning material to satisfy their needs. Technology Enhanced Learning (TEL) is an area which covers all technologies that improve students learning. Effective Personal Learning Recommendation Systems (PLRS) will not only reduce this burden of information overload by recommending the relevant learning material to the students of their interest, but also provide them with "right" information at the "right" time and in the "right" way. In this paper, we first present a detailed analysis of existing TEL recommendation systems and identify the challenges that exist for developing and evaluating the datasets. Then, we propose an architecture for developing a PLRS that aims to support students via a Learning Management System (LMS) to find relevant material in order to enhance student learning experience. Also we proposes a methodology for building our own collaborative dataset via learning management systems (LMS) and educational repositories. This dataset will enhance student learning by recommending learning materials from the former student's competence qualifications. The proposed dataset offer information on the usage of more than 19,296 resources from 628 courses apart from data from social learner networks (forums, blogs, wikis and chats), which constitutes another 3,600 stored files Finally, we also present some future challenges and a roadmap for developing TEL PLRSs.

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“…Recommender systems are crucial tools used nowadays in most of applications including Facebook, Twitter, Email services, News services and Hotel reservation applications [1]. Recommender systems provide users with suggested items (such as movies, friends, news, advertisements, products, etc.).…”

Section: Introductionmentioning

confidence: 99%

Novel GPU-Based Approach for Matrix Factorization using Stochastic Gradient Descent

Nassar¹,

El-Sayed²,

Taha³

2017

IJICR

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Recommender systems are crucial tools used in most of daily-based web applications. Matrix factorization is an advanced and efficient technique for recommender systems. Recently, Stochastic Gradient Descent (SGD) method is considered to be one of the most popular techniques for matrix factorization. SGD is a sequential algorithm, which is difficult to be parallelized for largescale problems. Nowadays, researches focus on efficiently parallelizing SGD. In this research, we propose a novel GPU approach for parallelizing SGD. The proposed approach is more efficient than recent parallel approaches because it utilizes GPU, reducing non-coalesced access of global memory and achieving load balance of threads. In addition, it does not require any sorting and/or data shuffling as preprocessing phase. Although platform used for implementation of the proposed approach is old, the implementation demonstrates 12.5x speedup over state-of-the-art GPU method, BSGD.

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Recommendation System: State of the Art Approach

Cited by 23 publications

References 24 publications

Adaptation using Machine Learning for Personalized eLearning Environment based on Students Preference

Adaptation using Machine Learning for Personalized eLearning Environment based on Students Preference

A Personalized Learning Recommendation System Architecture for Learning Management System

Novel GPU-Based Approach for Matrix Factorization using Stochastic Gradient Descent

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