Sequential recommendation aims to offer potentially interesting products to users by
capturing their historical sequence of interacted items. Although it has facilitated extensive …

In recent years, fashion e-commerce has become more and more popular. Since there are
so many fashion products provided by e-commerce retailers, it is necessary to provide …

A remarkable progress in sequential recommendation field lies on deep learning
techniques, where deep learning was widely used to capture user preference from behavior …

Predicting the next item that users may engage in is a key task of recommender systems,
and many methods have been proposed to deal with this task from different aspects …

Modeling of users and items is essential for accurate recommendations. Traditional methods
focused only on users' behavior data for recommendation. Several recent methods …

Attributes of items carry useful information for accurate recommendations. Existing methods
which tried to use items' attributes relied on either 1) feature-level compression which may …

Today, the ability to track users' sequence of online activities, makes identifying their
evolving preferences for recommendation practicable. However, despite the myriad of …

Current recommender systems often take fusion factors into consideration to realize
personalize point‐of‐interest (POI) recommendation. Historical behavior records and …

Recommender systems have been developed at different levels with different approaches to
help resolve the challenge of choice making due to the abundance and variety of …

Content-based movie recommendation systems mainly use the information from the movie
metadata to provide users with personalized movie recommendations based on their …