13 research outputs found
Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic World
No full textExtended 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}
Conversational Recommender Systems Using Generative Models (Gen-CRS): Literature Review
No full text<h2>Description</h2>
<p>This dataset contains a curated list of 49 research papers focused on Conversational Recommender Systems using Generative Models (Gen-CRS). The collection covers publications from 2018 to 2025 and reflects the rapid evolution of generative approaches in conversational recommendation scenarios.</p>
<p><br>The dataset was compiled in the context of the literature review “<a href="https://www.researchgate.net/publication/398319946_Conversational_Recommender_Systems_Using_Generative_Models_Gen-CRS_A_Literature_Review" target="_blank" rel="noopener">Conversational Recommender Systems Using Generative Models (Gen-CRS): A Literature Review</a>” and the tutorial “<a href="https://dl.acm.org/doi/10.1145/3705328.3748010" target="_blank" rel="noopener">A Tutorial on Recent Advances in Generative Conversational Recommender Systems</a>”, presented at the <a href="https://recsys.acm.org/recsys25/tutorials/" target="_blank" rel="noopener">ACM RecSys conference 2025</a>. It serves as the bibliographic foundation for both contributions and is intended to support transparency, reproducibility, and further research in this area.</p>
<p>Each entry in the dataset corresponds to a single paper relevant to Gen-CRS, and the selection process, collection methodology, and inclusion criteria are provided in the accompanying literature review paper.</p>
<h2>Dataset Structure</h2>
<p>The dataset is organized in a tabular format, where each row corresponds to a single publication included in the literature collection. Rows contain the essential bibliographic metadata required to identify and retrieve the original paper.</p>
<h3>The dataset includes the following columns:</h3>
<ul>
<li><strong>Paper title:</strong> Full title of the publication</li>
<li><strong>Author(s):</strong> Names of all authors as reported in the original paper</li>
<li><strong>Year:</strong> Year in which the paper was published</li>
<li><strong>Published at:</strong> Conference, workshop, journal, or other venue where the work appeared</li>
<li><strong>Reference Link/DOI:</strong> Persistent link to access the published document (e.g., DOI, publisher URL, or preprint reference)</li>
</ul>
Towards Individual and Multistakeholder Fairness in Tourism Recommender Systems
Get PDFThis position paper summarizes our published review on individual and
multistakeholder fairness in Tourism Recommender Systems (TRS). Recently, there
has been growing attention to fairness considerations in recommender systems
(RS). It has been acknowledged in research that fairness in RS is often closely
tied to the presence of multiple stakeholders, such as end users, item
providers, and platforms, as it raises concerns for the fair treatment of all
parties involved. Hence, fairness in RS is a multi-faceted concept that
requires consideration of the perspectives and needs of the different
stakeholders to ensure fair outcomes for them. However, there may often be
instances where achieving the goals of one stakeholder could conflict with
those of another, resulting in trade-offs.
In this paper, we emphasized addressing the unique challenges of ensuring
fairness in RS within the tourism domain. We aimed to discuss potential
strategies for mitigating the aforementioned challenges and examine the
applicability of solutions from other domains to tackle fairness issues in
tourism. By exploring cross-domain approaches and strategies for incorporating
S-Fairness, we can uncover valuable insights and determine how these solutions
can be adapted and implemented effectively in the context of tourism to enhance
fairness in RS.Comment: Position Paper for FAcctRec 2023 at RecSys 202
A User Interface Study on Sustainable City Trip Recommendations
Get PDFThe importance of promoting sustainable and environmentally responsible
practices is becoming increasingly recognized in all domains, including
tourism. The impact of tourism extends beyond its immediate stakeholders and
affects passive participants such as the environment, local businesses, and
residents. City trips, in particular, offer significant opportunities to
encourage sustainable tourism practices by directing travelers towards
destinations that minimize environmental impact while providing enriching
experiences. Tourism Recommender Systems (TRS) can play a critical role in
this. By integrating sustainability features in TRS, travelers can be guided
towards destinations that meet their preferences and align with sustainability
objectives.
This paper investigates how different user interface design elements affect
the promotion of sustainable city trip choices. We explore the impact of
various features on user decisions, including sustainability labels for
transportation modes and their emissions, popularity indicators for
destinations, seasonality labels reflecting crowd levels for specific months,
and an overall sustainability composite score. Through a user study involving
mockups, participants evaluated the helpfulness of these features in guiding
them toward more sustainable travel options.
Our findings indicate that sustainability labels significantly influence
users towards lower-carbon footprint options, while popularity and seasonality
indicators guide users to less crowded and more seasonally appropriate
destinations. This study emphasizes the importance of providing users with
clear and informative sustainability information, which can help them make more
sustainable travel choices. It lays the groundwork for future applications that
can recommend sustainable destinations in real-time
Differential Difference Current Conveyor (DDCC) Based Schmitt Trigger Circuit & Its Application
Get PDFA new voltage mode Schmitt trigger and its application using Differential Difference Current Conveyor (DDCC) is presented in this paper. The proposed circuit has a single DDCC block & two passive components. This circuit does not have any matching conditions. Here two passive components are used and one of them is grounded .The proposed circuit is simulated on SPICE platfor
Design of Capacitor Controlled Oscillator with the help of Operational Trans Resistance Amplifier
Get PDFThis research paper introduces a fully controllable Oscillator and Operational Trans Resistance Amplifier (OTRA)and six passive components consist of three Resistors And three Capacitors . Workability of all thesimulators are tested by 0.5?m CMOS Technology
LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization
No full textLarge Language Models (LLMs) are transforming personalized services by enabling adaptive, context-Aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains
