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Found 10 papers, 7 papers with code
Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance.
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
Recommender systems are prominent examples of these machine learning (ML) systems that aid users in making decisions.
Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems
Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues.
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction.
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.
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 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.
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.
