A Systematic Review of Recommendation System Based on Deep Learning Methods

Jingjing Wang; Lap Kei Lee; Nga In Wu

doi:10.1007/978-3-031-22018-0_12

A Systematic Review of Recommendation System Based on Deep Learning Methods

Jingjing Wang, Lap Kei Lee, Nga In Wu

Electronic Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

2 Citations (Scopus)

Abstract

Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.

Original language	English
Title of host publication	Lecture Notes in Networks and Systems
Pages	122-133
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-031-22018-0_12
Publication status	Published - 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	599 LNNS

Keywords

Deep learning
Recommender system
Survey
Systematic literature review

Access to Document

10.1007/978-3-031-22018-0_12

Cite this

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