2 citations · 2 across the 4 of their papers we have counts for
4 papers
The Bandit's Blind Spot: The Critical Role of User State Representation in Recommender Systems
Pedro R. Pires, Gregorio F. Azevedo, Rafael T. Sereicikas +2
With the increasing availability of online information, recommender systems have become an important tool for many web-based systems. Due to the continuous aspect of recommendation…
Learning Behaviorally Grounded Item Embeddings via Personalized Temporal Contexts
Rafael T. Sereicikas, Pedro R. Pires, Gregorio F. Azevedo +1
Effective user modeling requires distinguishing between short-term and long-term preference evolution. While item embeddings have become a key component of recommender systems, sta…
Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
Pedro R. Pires, Rafael T. Sereicikas, Gregorio F. Azevedo +1
In recent years, neural networks and other complex models have dominated recommender systems, often setting new benchmarks for state-of-the-art performance. Yet, despite these adva…
Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation
Pedro R. Pires, Gregorio F. Azevedo, Pietro L. Campos +2
Multi-Armed Bandit (MAB) algorithms are widely used in recommender systems that require continuous, incremental learning. A core aspect of MABs is the exploration-exploitation trad…