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Found 10 papers, 1 papers with code
Accuracy meets Diversity in a News Recommender System
We customize an augmented vector for each query and news item to introduce information interaction between the two towers.
Progress in Privacy Protection: A Review of Privacy Preserving Techniques in Recommender Systems, Edge Computing, and Cloud Computing
As digital technology evolves, the increasing use of connected devices brings both challenges and opportunities in the areas of mobile crowdsourcing, edge computing, and recommender systems.
NBIAS: A Natural Language Processing Framework for Bias Identification in Text
Therefore, it is crucial to detect and remove these biases to ensure the fair and ethical use of data.
Fairness in Machine Learning meets with Equity in Healthcare
With the growing utilization of machine learning in healthcare, there is increasing potential to enhance healthcare outcomes.
Leveraging Foundation Models for Clinical Text Analysis
Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies.
Addressing Biases in the Texts using an End-to-End Pipeline Approach
The concept of fairness is gaining popularity in academia and industry.
BERT4Loc: BERT for Location -- POI Recommender System
Our model combines location information and user preferences to provide more relevant recommendations compared to models that predict the next POI in a sequence.
An Approach to Ensure Fairness in News Articles
This paper introduces Dbias, which is a Python package to ensure fairness in news articles.
Improving Rating and Relevance with Point-of-Interest Recommender System
In this paper, we propose using user and item information and auxiliary information to improve the recommendation modelling in a retrieval system.
Detecting Fake Points of Interest from Location Data
The ground truth labels are obtained through real-world data, and the fake data is generated using an API, so we get a dataset with both the real and fake labels on the location data.
