Deep Recommender Systems Utilizing Side Information

Amit Livne

doi:10.1145/3437963.3441669

Deep Recommender Systems Utilizing Side Information

Amit Livne

Department of Software and Information Systems Engineering

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

Abstract

Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.

Original language	English
Title of host publication	WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining
Publisher	Association for Computing Machinery, Inc
Pages	1111-1112
Number of pages	2
ISBN (Electronic)	9781450382977
DOIs	https://doi.org/10.1145/3437963.3441669
State	Published - 3 Aug 2021
Event	14th ACM International Conference on Web Search and Data Mining, WSDM 2021 - Virtual, Online, Israel Duration: 8 Mar 2021 → 12 Mar 2021

Publication series

Name	WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Conference

Conference	14th ACM International Conference on Web Search and Data Mining, WSDM 2021
Country/Territory	Israel
City	Virtual, Online
Period	8/03/21 → 12/03/21

Keywords

neural networks
recommendation systems
side information

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Software

Access to Document

10.1145/3437963.3441669

Cite this

@inproceedings{8541b645947d420b8e0ef0ba66efaf4e,

title = "Deep Recommender Systems Utilizing Side Information",

abstract = "Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.",

keywords = "neural networks, recommendation systems, side information",

author = "Amit Livne",

note = "Publisher Copyright: {\textcopyright} 2021 Owner/Author.; 14th ACM International Conference on Web Search and Data Mining, WSDM 2021 ; Conference date: 08-03-2021 Through 12-03-2021",

year = "2021",

month = aug,

day = "3",

doi = "10.1145/3437963.3441669",

language = "English",

series = "WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining",

publisher = "Association for Computing Machinery, Inc",

pages = "1111--1112",

booktitle = "WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining",

}

Livne, A 2021, Deep Recommender Systems Utilizing Side Information. in WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc, pp. 1111-1112, 14th ACM International Conference on Web Search and Data Mining, WSDM 2021, Virtual, Online, Israel, 8/03/21. https://doi.org/10.1145/3437963.3441669

Deep Recommender Systems Utilizing Side Information. / Livne, Amit.
WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, Inc, 2021. p. 1111-1112 (WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining).

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

TY - GEN

T1 - Deep Recommender Systems Utilizing Side Information

AU - Livne, Amit

PY - 2021/8/3

Y1 - 2021/8/3

N2 - Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.

AB - Recommendation Systems (RS) are designed to assist users in decision making by recommending the most appropriate information or products for them. Nonetheless, many RS suffer from limitations such as data sparsity and cold-start. Side information (SI) can be integrated into a recommender system to tackle these limitations. In my Ph.D. research, I seek to build on and extend the use of SI for RS. Specifically, I propose new types and representations of SI and develop new methods to integrate SI into RS to boost its performance. This paper presents the conceptual foundation and motivation of my Ph.D. research.

KW - neural networks

KW - recommendation systems

KW - side information

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U2 - 10.1145/3437963.3441669

DO - 10.1145/3437963.3441669

M3 - Conference contribution

AN - SCOPUS:85103014037

T3 - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

SP - 1111

EP - 1112

BT - WSDM 2021 - Proceedings of the 14th ACM International Conference on Web Search and Data Mining

PB - Association for Computing Machinery, Inc

T2 - 14th ACM International Conference on Web Search and Data Mining, WSDM 2021

Y2 - 8 March 2021 through 12 March 2021

ER -

Deep Recommender Systems Utilizing Side Information

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this