@inproceedings{82e26c6c651449dfa5a1ae1402adff69,
title = "Exploiting side information for recommendation",
abstract = "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.",
author = "Qing Guo and Zhu Sun and Theng, {Yin Leng}",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-19274-7_46",
language = "English",
isbn = "9783030192730",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-VDI-Verlag GmbH & Co. KG",
pages = "569--573",
editor = "Maxim Bakaev and Flavius Frasincar and In-Young Ko",
booktitle = "Web Engineering",
address = "Germany",
note = "19th International Conference on Web Engineering, ICWE 2019 ; Conference date: 11-06-2019 Through 14-06-2019",
}