145 citations · 456 across the 34 of their papers we have counts for
9 papers · 1 filter
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…