Recent Developments in Recommender Systems

Jia-Ming Low*, Ian K. T. Tan, Choo-Yee Ting

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


With greater penetration of online services, the use of recommender systems to predict users’ propensity for continuous engagement becomes crucial in ensuring maximum revenue. There are many challenges, such as the cold start problem and data sparsity, that are continuously being addressed by a myriad of techniques in recommender systems. This paper provides insights into the trends of the techniques used for recommender systems and the challenges they address. With the insights; deep learning, matrix factorization or a combination of both can be used in addressing the data sparsity challenge.

Original languageEnglish
Title of host publicationMulti-disciplinary Trends in Artificial Intelligence. MIWAI 2019
EditorsRapeeporn Chamchong, Kok Wai Wong
Number of pages14
ISBN (Electronic)9783030337094
ISBN (Print)9783030337087
Publication statusPublished - 21 Oct 2019
Event13th Multi-disciplinary International Conference on Artificial Intelligence 2019 - Kuala Lumpur, Malaysia
Duration: 17 Nov 201919 Nov 2019

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th Multi-disciplinary International Conference on Artificial Intelligence 2019
Abbreviated titleMIWAI 2019
CityKuala Lumpur

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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