Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
Open accessAbstract
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.
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A lack of societal fairness can raise sustainability issues.
“A lack of it can raise sustainability issues [47-49].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [49]
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- Authors
- Patro, G. K., Chakraborty, A., Banerjee, A., & Ganguly, N.
- Affiliations
- Indian Institute of Technology Kharagpur (IN)
- Max Planck Institute for Software Systems (DE)
- Technical University of Munich (DE)
- Venue
- Fourteenth ACM Conference on Recommender Systems
- Publisher
- ACM
- Published
- 2020-09-22
- Pages
- 358-367
- OpenAlex
- W3088062397
- Cited by
- 21
- References
- 43
- Citations by year
- 2025: 6 · 2024: 3 · 2023: 6 · 2022: 2 · 2021: 3 · 2020: 1
- FWCI
- 2.28
- Type
- conference paper
- Language
- en
- Open access
- green
- zenodo.org
- Locations
- ACM Conference on Recommender Systems (RecSys) (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Game Theory and Voting Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- COVID-19 pandemic, social distancing, footfall, overcrowding, multi-objective optimization, bipartite matching, Yelp, customer safety
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