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Deep Learning Methods on Recommender System: A Survey of State-of-the-art

by Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 162 - Number 10
Year of Publication: 2017
Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui
10.5120/ijca2017913361

Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui . Deep Learning Methods on Recommender System: A Survey of State-of-the-art. International Journal of Computer Applications. 162, 10 ( Mar 2017), 17-22. DOI=10.5120/ijca2017913361

@article{ 10.5120/ijca2017913361,
author = { Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui },
title = { Deep Learning Methods on Recommender System: A Survey of State-of-the-art },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 162 },
number = { 10 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 17-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume162/number10/27279-2017913361/ },
doi = { 10.5120/ijca2017913361 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:08:39.339754+05:30
%A Basiliyos Tilahun Betru
%A Charles Awono Onana
%A Bernabe Batchakui
%T Deep Learning Methods on Recommender System: A Survey of State-of-the-art
%J International Journal of Computer Applications
%@ 0975-8887
%V 162
%N 10
%P 17-22
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The advancement in technology accelerated and opened availability of various alternatives to make a choice in every domain. In the era of big data it is a tedious and time consuming task to evaluate the features of a large amount of information provided to make a choice. One solution to ease this overload problem is building recommender system that can process a large amount of data and support users’ decision making ability. In this paper different traditional recommendation techniques, deep learning approaches for recommender system and survey of deep learning techniques on recommender system are presented. A variety of techniques have been proposed to perform recommendation, including content based, collaborative and hybrid recommenders. Due to the limitation of the traditional recommendation methods in obtaining accurate result a deep learning approach is introduced both for collaborative and content based approaches that will enable the model to learn different features of users and items automatically to improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender systems have not been fully exploited. With the help of the advantage of deep learning in modeling different types of data, deep recommender systems can better understand users’ demand to further improve quality of recommendation.

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Index Terms

Computer Science
Information Sciences

Keywords

Recommender system deep learning big data decision making collaborative filtering hybrid recommender.

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