145 citations · 456 across the 34 of their papers we have counts for
38 papers
Personalized Recommender Systems for Gym Workouts: A Reinforcement Learning Approach
Roan Rosema, Helma Torkamaan, Masoud Mansoury
Workout recommender systems aim to help gym users complete effective and engaging training sessions. However, recommending exercises alone is insufficient, as a practical system mu…
From Top-1 to Top-K: A Reproducibility Study and Benchmarking of Counterfactual Explanations for Recommender Systems
Quang-Huy Nguyen, Thanh-Hai Nguyen, Khac-Manh Thai +6
Counterfactual explanations (CEs) provide an intuitive way to understand recommender systems by identifying minimal modifications to user-item interactions that alter recommendatio…
From Insight to Intervention: Interpretable Neuron Steering for Controlling Popularity Bias in Recommender Systems
Parviz Ahmadov, Masoud Mansoury
Popularity bias is a pervasive challenge in recommender systems, where a few popular items dominate attention while the majority of less popular items remain underexposed. This imb…
The Unfairness of Multifactorial Bias in Recommendation
Masoud Mansoury, Jin Huang, Mykola Pechenizkiy +2
Popularity bias and positivity bias are two prominent sources of bias in recommender systems. Both arise from input data, propagate through recommendation models, and lead to unfai…
Effectiveness of LLMs in Temporal User Profiling for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user pro…
Mitigating Popularity Bias in Counterfactual Explanations using Large Language Models
Arjan Hasami, Masoud Mansoury
Counterfactual explanations (CFEs) offer a tangible and actionable way to explain recommendations by showing users a "what-if" scenario that demonstrates how small changes in their…
Using LLMs to Capture Users' Temporal Context for Recommendation
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Effective recommender systems demand dynamic user understanding, especially in complex, evolving environments. Traditional user profiling often fails to capture the nuanced, tempor…
Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Accurately modeling user preferences is crucial for improving the performance of content-based recommender systems. Existing approaches often rely on simplistic user profiling meth…
Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints
Daniel Daza, Alberto Bernardi, Luca Costabello +4
Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such answers cannot be reached by di…
RaMen: Multi-Strategy Multi-Modal Learning for Bundle Construction
Huy-Son Nguyen, Quang-Huy Nguyen, Duc-Hoang Pham +5
Existing studies on bundle construction have relied merely on user feedback via bipartite graphs or enhanced item representations using semantic information. These approaches fail…
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
Huy-Son Nguyen, Yuanna Liu, Masoud Mansoury +3
Recommender systems are known to exhibit fairness issues, particularly on the product side, where products and their associated suppliers receive unequal exposure in recommended re…
Towards Explainable Temporal User Profiling with LLMs
Milad Sabouri, Masoud Mansoury, Kun Lin +1
Accurately modeling user preferences is vital not only for improving recommendation performance but also for enhancing transparency in recommender systems. Conventional user profil…
Towards Carbon Footprint-Aware Recommender Systems for Greener Item Recommendation
Raoul Kalisvaart, Masoud Mansoury, Alan Hanjalic +1
The commodity and widespread use of online shopping are having an unprecedented impact on climate, with emission figures from key actors that are easily comparable to those of a la…
Correcting Popularity Bias in Recommender Systems via Item Loss Equalization
Juno Prent, Masoud Mansoury
Recommender Systems (RS) often suffer from popularity bias, where a small set of popular items dominate the recommendation results due to their high interaction rates, leaving many…
Mitigating Exposure Bias in Online Learning to Rank Recommendation: A Novel Reward Model for Cascading Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This bias becomes particularly probl…
Beyond Static Calibration: The Impact of User Preference Dynamics on Calibrated Recommendation
Kun Lin, Masoud Mansoury, Farzad Eskandanian +2
Calibration in recommender systems is an important performance criterion that ensures consistency between the distribution of user preference categories and that of recommendations…
Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems
Jin Huang, Harrie Oosterhuis, Masoud Mansoury +2
Two typical forms of bias in user interaction data with recommender systems (RSs) are popularity bias and positivity bias, which manifest themselves as the over-representation of i…
Potential Factors Leading to Popularity Unfairness in Recommender Systems: A User-Centered Analysis
Masoud Mansoury, Finn Duijvestijn, Imane Mourabet
Popularity bias is a well-known issue in recommender systems where few popular items are over-represented in the input data, while majority of other less popular items are under-re…
Predictive Uncertainty-based Bias Mitigation in Ranking
Maria Heuss, Daniel Cohen, Masoud Mansoury +2
Societal biases that are contained in retrieved documents have received increased interest. Such biases, which are often prevalent in the training data and learned by the model, ca…
Career Path Recommendations for Long-term Income Maximization: A Reinforcement Learning Approach
Spyros Avlonitis, Dor Lavi, Masoud Mansoury +1
This study explores the potential of reinforcement learning algorithms to enhance career planning processes. Leveraging data from Randstad The Netherlands, the study simulates the…
Fairness of Exposure in Dynamic Recommendation
Masoud Mansoury, Bamshad Mobasher
Exposure bias is a well-known issue in recommender systems where the exposure is not fairly distributed among items in the recommendation results. This is especially problematic wh…
Exposure-Aware Recommendation using Contextual Bandits
Masoud Mansoury, Bamshad Mobasher, Herke van Hoof
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when…
Understanding and Mitigating Multi-Sided Exposure Bias in Recommender Systems
Masoud Mansoury
Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendatio…
Unbiased Cascade Bandits: Mitigating Exposure Bias in Online Learning to Rank Recommendation
Masoud Mansoury, Himan Abdollahpouri, Bamshad Mobasher +3
Exposure bias is a well-known issue in recommender systems where items and suppliers are not equally represented in the recommendation results. This is especially problematic when…