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

<p><strong>Extended Version of The Paper:</strong></p> <pre><code class="language-markdown">Towards_Safety_and_Sustainability_Extended.pdf</code></pre> <p><strong>Dataset Information:</strong></p> <p>List of files</p> <pre><code class="language-markdown">Customer_Choice_Survey.csv NYC_Google.csv NYC_Yelp.csv SF_Google.csv SF_Yelp.csv</code></pre> <p>Field Details in Each File</p> <ol> <li><strong>&quot;Customer_Choice_Survey.csv&quot;:</strong>&nbsp;Local recommendations received on Google Local (Google Maps) for different customer locations in New York and San Francisco. <pre><code class="language-markdown">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.</code></pre> <p>&nbsp;</p> </li> <li><strong>&quot;NYC_Google.csv&quot; and &quot;SF_Google.csv&quot;:</strong>&nbsp;Local recommendations received on Yelp for different customer locations in New York and San Francisco. <pre><code class="language-markdown">"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</code></pre> <p>&nbsp;</p> </li> <li>&quot;NYC_Yelp.csv&quot; and &quot;SF_Yelp.csv&quot; <pre><code class="language-markdown">"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</code></pre> <p>&nbsp;</p> </li> </ol> <p>Link to Code Repository:<br> <a href="https://github.com/gourabkumarpatro/pandemic-aware_local_recommendation">Pandemic-Aware Local Recommendation</a></p> <p><strong>Citation Information:</strong><br> Please cite the following paper if you use this dataset.<br> <br> <strong>&quot;<em>Towards Sustainability and Safety: Designing Local Recommendations for Post-pandemic World</em>&quot;</strong><br> Gourab K Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly.<br> In proceedings of Fourteenth ACM Conference on Recommender Systems (RecSys-2020), Virtual Event, Brazil.</p> <p>You can also use the following <strong>bibtex</strong>.</p> <pre><code class="language-markdown">@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} }</code></pre> <p>&nbsp;</p>