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Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World

EasyChair Preprint no. 4290

21 pagesDate: September 28, 2020


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.

Keyphrases: bipartite matching, COVID-19, Google Local, Local recommendation, safety, Sustainability, Yelp

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Gourab K Patro and Abhijnan Chakraborty and Ashmi Banerjee and Niloy Ganguly},
  title = {Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World},
  howpublished = {EasyChair Preprint no. 4290},

  year = {EasyChair, 2020}}
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