Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World Gourab K Patro Indian Institute of Technology Kharagpur, India Abhijnan Chakraborty Max-Planck Institute for Software Systems, Germany Ashmi Banerjee Technical University of Munich, Germany Niloy Ganguly Indian Institute of Technology Kharagpur, India 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 oper- ate 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 signifcant loss in recommendation utility for the cus- tomers. 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. Ex- tensive experiments over multiple real-world datasets reveal the efcacy of our approach along with the three-way control over sustainability, safety, and utility goals. CCS CONCEPTS · Information systems → Recommender systems. KEYWORDS Safety, Sustainability, COVID-19, Social Distancing, Local Recom- mendation, Bipartite Matching, Yelp, Google Local ACM Reference Format: Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, and Niloy Ganguly. 2020. Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World. In Fourteenth ACM Conference on Recommender Systems (RecSys ’20), September 22ś26, 2020, Virtual Event, Brazil. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3383313.3412251 1 INTRODUCTION With the proliferation of GPS-enabled smartphones, local recom- mendation platforms like Google Local (rendered on Google Maps), Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. RecSys ’20, September 22ś26, 2020, Virtual Event, Brazil © 2020 Copyright held by the owner/author(s). Publication rights licensed to ACM. ACM ISBN 978-1-4503-7583-2/20/09. . . $15.00 https://doi.org/10.1145/3383313.3412251 Yelp, Zomato, etc. have experienced massive growth in the last few years. For example, since 2011, the use of łnear me" service on Google Local has grown by an astounding 3400% [39]. These platforms recommend nearby/local businesses (restaurants, cafes, stores, malls, etc.) to customers based on their physical locations and other inferred preferences, and in 2016, customers have visited around 1.5 billion businesses every month using these location- based services [39]. However, these regular customer-business phys- ical interactions have been severely impacted due to the spread of highly contagious SARS-CoV-2 and the resultant COVID-19 pandemic. To limit the viral spread, many countries enforced com- plete/partial lockdowns for an extended period leading to the clo- sure of several businesses, and even after reopening, strict adher- ence to social distancing guidelines is an absolute requirement to ensure safety of the customers. Considering the extensive use and infuence of local recommendation platforms in attracting cus- tomers to local businesses, in this paper, we propose to design local recommendation systems which can help in achieving safety for customers as well as economic sustainability for businesses in the post-pandemic world. Traditionally, these platforms have used diferent data-driven models [12, 21, 24, 28, 34, 44] to estimate relevance of local busi- nesses to individual customers, and then recommended k most relevant results to them. By gathering data from Google Local and Yelp, we show that such pre-COVID recommendation practices can cause a high inequality in the exposure (visibility) of local busi- nesses, where a few businesses can end up receiving a large fraction of total exposure while the remaining businesses receive a very low exposure. This could, on one hand, lead to overcrowding at some businesses, compromising customer safety. On the other hand, it could result in a very low footfall at other businesses, question- ing their sustainability in the ongoing scenario (detailed in ğ3). A simple answer to these concerns would be to fnd a way which can reduce inequality in business exposures. However, using naive methods to reduce exposure inequality (e.g., poorest- k : recommend- ing k least exposed businesses to customers) may result in a huge loss in customer utility (detailed in ğ3), thereby rendering the plat- form inefcient for customers. Therefore, our focus on safety and sustainability need to go hand-in-hand with customer utility. We formally defne the desired properties for sustainability, safety, and utility in section 3.1. For sustainability, we propose to use a minimum exposure guarantee for every business, and for safety, we propose to keep the exposure of a business below a cer- tain maximum limit which is proportional to its safe capacity. As we observe in the case of poorest- k , there is a clear tradeof between utility and sustainability/safety, and simultaneously satisfying all 358