Sequential recommender systems predict items that may interest users by modeling their
preferences based on historical interactions. Traditional sequential recommendation …

The long-tailed problem is a long-standing challenge in Sequential Recommender Systems
(SRS) in which the problem exists in terms of both users and items. While many existing …

Item representation learning (IRL) plays an essential role in recommender systems,
especially for sequential recommendation. Traditional sequential recommendation models …

Deep learning has brought significant breakthroughs in sequential recommendation (SR) for
capturing dynamic user interests. A series of recent research revealed that models with more …

The goal of sequential recommendation is to predict the next item that a user would like to
interact with, by capturing her dynamic historical behaviors. However, most existing …

Recently advancements in deep learning models have significantly facilitated the
development of sequential recommender systems (SRS). However, the current deep model …

Sequential recommender systems (SRS) aim to predict users' subsequent choices based on
their historical interactions and have found applications in diverse fields such as e …

Modern recommendation systems are mostly based on implicit feedback data which can be
quite noisy due to false positives (FPs) caused by many reasons, such as misclicks or quick …

Sequential recommendation systems (SRS) serve the purpose of predicting users'
subsequent preferences based on their past interactions and have been applied across …

The popularity of items from the existing rating dataset has a significant impact on
Recommendation Systems (RS). There is also a need to minimize bias of the …