Recommender systems are powerful tools that successfully apply data mining and machine
learning techniques. Traditionally, these systems focused on predicting a single interaction …

Sequential recommendation is dedicated to offering items of interest for users based on their
history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items …

Temporal recommendation methods can achieve superior accuracy due to updating
user/item embeddings continuously once obtaining new interactions. However, the …

Graph Signal Processing (GSP) has proven to be a highly effective and efficient tool for
predicting user future interactions in recommender systems. However, current GSP methods …

This review explores machine unlearning (MUL) in recommendation systems, addressing
adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL …

The task of next basket recommendation is pivotal for recommender systems. It involves
predicting user actions, such as the next product purchase or movie selection, by exploring …

In the rapidly evolving field of artificial intelligence, transformer-based models have gained
significant attention in the context of Sequential Recommender Systems (SRSs) …

Los sistemas de recomendación juegan un papel fundamental en la tarea de filtrado y
recuperación de información. En un mundo con acceso a grandes cantidades de …