The COVID-19 pandemic has made it paramount to maintain social distance to limit the viral transmission probability. At the same time, local businesses (e.g., restaurants, cafes, stores, malls) need to operate to ensure their economic sustainability. Considering the wide usage of local recommendation platforms like Google Local and Yelp by customers to choose local businesses, we propose to design local recommendation systems which can help in achieving both safety and sustainability goals. Our investigation of existing local recommendation systems shows that they can lead to overcrowding at some businesses compromising customer safety, and very low footfall at other places threatening their economic sustainability. On the other hand, naive ways of ensuring safety and sustainability can cause significant loss in recommendation utility for the customers. Thus, we formally express the problem as a multi-objective optimization problem and solve by innovatively mapping it to a bipartite matching problem with polynomial time solutions. Extensive experiments over multiple real-world datasets reveal the efficacy of our approach along with the three-way control over sustainability, safety, and utility goals.
Description
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World | Fourteenth ACM Conference on Recommender Systems
%0 Conference Paper
%1 Patro_2020
%A Patro, Gourab K
%A Chakraborty, Abhijnan
%A Banerjee, Ashmi
%A Ganguly, Niloy
%B Fourteenth ACM Conference on Recommender Systems
%D 2020
%I ACM
%K fairness multi-stakeholder recommender recsys2020
%P 358-367
%R 10.1145/3383313.3412251
%T Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
%U https://doi.org/10.1145%2F3383313.3412251
%X The COVID-19 pandemic has made it paramount to maintain social distance to limit the viral transmission probability. At the same time, local businesses (e.g., restaurants, cafes, stores, malls) need to operate to ensure their economic sustainability. Considering the wide usage of local recommendation platforms like Google Local and Yelp by customers to choose local businesses, we propose to design local recommendation systems which can help in achieving both safety and sustainability goals. Our investigation of existing local recommendation systems shows that they can lead to overcrowding at some businesses compromising customer safety, and very low footfall at other places threatening their economic sustainability. On the other hand, naive ways of ensuring safety and sustainability can cause significant loss in recommendation utility for the customers. Thus, we formally express the problem as a multi-objective optimization problem and solve by innovatively mapping it to a bipartite matching problem with polynomial time solutions. Extensive experiments over multiple real-world datasets reveal the efficacy of our approach along with the three-way control over sustainability, safety, and utility goals.
@inproceedings{Patro_2020,
abstract = {The COVID-19 pandemic has made it paramount to maintain social distance to limit the viral transmission probability. At the same time, local businesses (e.g., restaurants, cafes, stores, malls) need to operate to ensure their economic sustainability. Considering the wide usage of local recommendation platforms like Google Local and Yelp by customers to choose local businesses, we propose to design local recommendation systems which can help in achieving both safety and sustainability goals. Our investigation of existing local recommendation systems shows that they can lead to overcrowding at some businesses compromising customer safety, and very low footfall at other places threatening their economic sustainability. On the other hand, naive ways of ensuring safety and sustainability can cause significant loss in recommendation utility for the customers. Thus, we formally express the problem as a multi-objective optimization problem and solve by innovatively mapping it to a bipartite matching problem with polynomial time solutions. Extensive experiments over multiple real-world datasets reveal the efficacy of our approach along with the three-way control over sustainability, safety, and utility goals.},
added-at = {2020-09-28T23:22:36.000+0200},
author = {Patro, Gourab K and Chakraborty, Abhijnan and Banerjee, Ashmi and Ganguly, Niloy},
biburl = {https://www.bibsonomy.org/bibtex/229b7f6ec8c1ac1c280b8d788268812ab/brusilovsky},
booktitle = {Fourteenth {ACM} Conference on Recommender Systems},
description = {Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World | Fourteenth ACM Conference on Recommender Systems},
doi = {10.1145/3383313.3412251},
interhash = {40c2bd628470f56485070f488d0dda27},
intrahash = {29b7f6ec8c1ac1c280b8d788268812ab},
keywords = {fairness multi-stakeholder recommender recsys2020},
month = sep,
pages = {358-367},
publisher = {{ACM}},
timestamp = {2020-09-28T23:22:36.000+0200},
title = {Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World},
url = {https://doi.org/10.1145%2F3383313.3412251},
year = 2020
}