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Found 14 papers, 9 papers with code
Towards Confidence-aware Calibrated Recommendation
Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data.
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation
Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI) recommender systems.
ViralBERT: A User Focused BERT-Based Approach to Virality Prediction
We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field.
Experiments on Generalizability of User-Oriented Fairness in Recommender Systems
In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method.
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical, or economic consequences.
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy (personalization) of recommendation as three angles of our study triangle.
The Unfairness of Popularity Bias in Book Recommendation
Furthermore, our study shows a tradeoff between personalization and unfairness of popularity bias in recommendation algorithms for users belonging to the Diverse and Bestseller groups, that is, algorithms with high capability of personalization suffer from the unfairness of popularity bias.
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation
The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e. g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets.
Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation
To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them.
Demographic Biases of Crowd Workers in Key Opinion Leaders Finding
Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions.
Mining Shape of Expertise: A Novel Approach Based on Convolutional Neural Network
An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective.
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem.
LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation
To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.
Category-Aware Location Embedding for Point-of-Interest Recommendation
With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation.
