Exploiting side information for recommendation

Qing Guo*, Zhu Sun, Yin Leng Theng

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)


Recommender systems have become extremely essential tools to help resolve the information overload problem for users. However, traditional recommendation techniques suffer from critical issues such as data sparsity and cold start problems. To address these issues, a great number of recommendation algorithms have been proposed by exploiting the side information. This tutorial aims to provide a comprehensive analysis of how to exploit various kinds of side information for improving recommendation performance. Specifically, we present the usage of side information from two perspectives: the representation and methodology. By this tutorial, researchers of recommender system would gain an in-depth understanding of how side information can be utilized for better recommendation performance.

Original languageEnglish
Title of host publicationWeb Engineering
Subtitle of host publication19th International Conference, ICWE 2019, Proceedings
EditorsMaxim Bakaev, Flavius Frasincar, In-Young Ko
Place of PublicationSwitzerland
PublisherSpringer-VDI-Verlag GmbH & Co. KG
Number of pages5
ISBN (Electronic)9783030192747
ISBN (Print)9783030192730
Publication statusPublished - 1 Jan 2019
Externally publishedYes
Event19th International Conference on Web Engineering, ICWE 2019 - Daejeon, Korea, Republic of
Duration: 11 Jun 201914 Jun 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11496 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th International Conference on Web Engineering, ICWE 2019
Country/TerritoryKorea, Republic of


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