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

Extended Version of The Paper: Towards_Safety_and_Sustainability_Extended.pdf Dataset Information: List of files Customer_Choice_Survey.csv NYC_Google.csv NYC_Yelp.csv SF_Google.csv SF_Yelp.csv Field Details in Each File "Customer_Choice_Survey.csv": Local recommendations received on Google Local (Google Maps) for different customer locations in New York and San Francisco. Each respondent was first asked some basic details. Then 7 rounds of ranking questions were asked. In each round, they were given a list of 10 restaurants with random combinations of rating, distance and cuisine. They were asked to rank top 5 one-by-one out of those 10 provided. This becomes evident from the question titles provided the file. "NYC_Google.csv" and "SF_Google.csv": Local recommendations received on Yelp for different customer locations in New York and San Francisco. "customer_location": location of the customer where she gets recommendation "rank": rank of the restaurant in the recommended list "id": restaurant's id internal to google "latitude": latitude of restaurant's geographic coordinates "longitude": longitude of restaurant's geographic coordinates "name": name of the resturant "price_level": cheap/costly level "rating": average rating of the restaurant "rating_count": number of ratings collected for the restaurant "address": address of the restaurant "NYC_Yelp.csv" and "SF_Yelp.csv" "customer_location": location of the customer where she gets recommendation "rank": rank of the restaurant in the recommended list "id": restaurant's id internal to yelp "latitude": latitude of restaurant's geographic coordinates "longitude": longitude of restaurant's geographic coordinates "name": name of the resturant "rating": average rating of the restaurant "rating_count": number of ratings collected for the restaurant "address": address of the restaurant "url": link to the restaurant's yelp page Link to Code Repository: Pandemic-Aware Local Recommendation Citation Information: Please cite the following paper if you use this dataset. "Towards Sustainability and Safety: Designing Local Recommendations for Post-pandemic World" Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly. In proceedings of Fourteenth ACM Conference on Recommender Systems (RecSys-2020), Virtual Event, Brazil. You can also use the following bibtex. @inproceedings{10.1145/3383313.3412251, author = {Patro, Gourab K and Chakraborty, Abhijnan and Banerjee, Ashmi and Ganguly, Niloy}, title = {Towards Safety and Sustainability: Designing Local Recommendations for Post-Pandemic World}, year = {2020}, isbn = {9781450375832}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3383313.3412251}, doi = {10.1145/3383313.3412251}, booktitle = {Fourteenth ACM Conference on Recommender Systems}, pages = {358–367}, numpages = {10}, keywords = {COVID-19, Local Recommendation, Google Local, Yelp, Safety, Social Distancing, Sustainability, Bipartite Matching}, location = {Virtual Event, Brazil}, series = {RecSys '20} }