A Systematic Review of Recommendation System Based on Deep Learning Methods

Jingjing Wang, Lap Kei Lee, Nga In Wu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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 languageEnglish
Title of host publicationLecture Notes in Networks and Systems
Pages122-133
Number of pages12
DOIs
Publication statusPublished - 2023

Publication series

NameLecture Notes in Networks and Systems
Volume599 LNNS

Keywords

  • Deep learning
  • Recommender system
  • Survey
  • Systematic literature review

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