The recommender system (RS) has been an integral toolkit of online services. They are
equipped with various deep learning techniques to model user preference based on …

Recommendation systems have become popular and effective tools to help users discover
their interesting items by modeling the user preference and item property based on implicit …

Multimodal recommender systems utilizing multimodal features (eg, images and textual
descriptions) typically show better recommendation accuracy than general recommendation …

The goal of sequential recommendation (SR) is to predict a user's potential interested items
based on her/his historical interaction sequences. Most existing sequential recommenders …

Multimedia recommendation has received much attention in recent years. It models user
preferences based on both behavior information and item multimodal information. Though …

Collaborative filtering (CF) is widely used to learn informative latent representations of users
and items from observed interactions. Existing CF-based methods commonly adopt negative …

This paper presents an open-source toolbox, MMRec for multimodal recommendation.
MMRec simplifies and canonicalizes the process of implementing and comparing …

Sequential recommendation systems often suffer from data sparsity, leading to suboptimal
performance. While multimodal content, such as images and text, has been utilized to …

The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled
personalized recommender systems to incorporate multiple modalities (such as visual …

Multi-modal recommendation systems, which integrate diverse types of information, have
gained widespread attention in recent years. However, compared to traditional collaborative …