Recently, embedding techniques have achieved impressive success in recommender
systems. However, the embedding techniques are data demanding and suffer from the cold …

In the field of sequential recommendation, deep learning--(DL) based methods have
received a lot of attention in the past few years and surpassed traditional models such as …

In location-based social networks (LBSNs), extensive user check-in data incorporating user
preferences for location is collected through Internet of Things devices, including cell …

Recommender systems aim to provide personalized services to users and are playing an
increasingly important role in our daily lives. The key of recommender systems is to predict …

Sequential recommendation has become a trending research topic for its capability to
capture dynamic user intents based on historical interaction sequence. To train a sequential …

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose
meters, electrocardiographs), which results in the generation of large amounts of data …

Sequential recommendation (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the user's historical …

Intelligence is the ability to learn from experience and use domain experts' knowledge to
adapt to new situations. In this context, an intelligent Recommender System should be able …

Sequential recommendation as an emerging topic has attracted increasing attention due to
its important practical significance. Models based on deep learning and attention …

Transformer models have achieved remarkable success in sequential recommender
systems (SRSs). However, computing the attention matrix in traditional dot-product attention …