Recommender Systems over Wireless: Challenges and Opportunities

Linqi Song; Christina Fragouli; Devavrat Shah

doi:10.1109/ITW.2018.8613423

Recommender Systems over Wireless: Challenges and Opportunities

Linqi Song^*
, Christina Fragouli
, Devavrat Shah

^*Corresponding author for this work

Department of Computer Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

9 Citations (Scopus)

Abstract

We consider wireless recommender systems that need to learn the user preferences (explore) and use them to accordingly decide what are the most profitable recommendations to make (exploit), under bandwidth constraints. We propose a graph-based scheme that leverages user side information and coding to efficiently exploit and explore over wireless, and evaluate its performance.

Original language	English
Title of host publication	2018 IEEE Information Theory Workshop (ITW)
Publisher	IEEE
ISBN (Electronic)	9781538635995, 9781538635988
ISBN (Print)	9781538636008
DOIs	https://doi.org/10.1109/ITW.2018.8613423
Publication status	Published - Nov 2018
Event	2018 IEEE Information Theory Workshop (ITW 2018): Sun Yat-sen Kaifeng Hotel - Guangzhou, China Duration: 25 Nov 2018 → 29 Nov 2018 http://www.itw2018.org/

Publication series

Name	IEEE Information Theory Workshop

Conference

Conference	2018 IEEE Information Theory Workshop (ITW 2018)
Abbreviated title	ITW 2018
Place	China
City	Guangzhou
Period	25/11/18 → 29/11/18
Internet address	http://www.itw2018.org/

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Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

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author = "Linqi Song and Christina Fragouli and Devavrat Shah",

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PY - 2018/11

Y1 - 2018/11

N2 - We consider wireless recommender systems that need to learn the user preferences (explore) and use them to accordingly decide what are the most profitable recommendations to make (exploit), under bandwidth constraints. We propose a graph-based scheme that leverages user side information and coding to efficiently exploit and explore over wireless, and evaluate its performance.

AB - We consider wireless recommender systems that need to learn the user preferences (explore) and use them to accordingly decide what are the most profitable recommendations to make (exploit), under bandwidth constraints. We propose a graph-based scheme that leverages user side information and coding to efficiently exploit and explore over wireless, and evaluate its performance.

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DO - 10.1109/ITW.2018.8613423

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781538636008

T3 - IEEE Information Theory Workshop

BT - 2018 IEEE Information Theory Workshop (ITW)

PB - IEEE

Y2 - 25 November 2018 through 29 November 2018

ER -

Recommender Systems over Wireless: Challenges and Opportunities

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