Collaborative Filtering, a popular method for recommendation engines, models its
predictions using past interactions between the entities in question (aka users/movies or …

Abstract Graph Convolutional Networks (GCN) have been recently employed as core
component in the construction of recommender system algorithms, interpreting user-item …

This paper presents LightLM, a lightweight Transformer-based language model for
generative recommendation. While Transformer-based generative modeling has gained …

Although many researchers of recommender systems have noted that encoding user-item
interactions based on DNNs promotes the performance of collaborative filtering, they ignore …

Inheriting from the basic idea of latent factor models like matrix factorization, current
collaborative filtering models focus on learning better latent representations of users and …

Many Deep Learning approaches solve complicated classification and regression problems
by hierarchically constructing complex features from the raw input data. Although a few …

In this paper, we propose the recommendation algorithm PNCF for neural networks. We
designed a pre-training task for a distributed representation of embeddings based on many …

Recently, deep learning techniques are widely used inthe field of multimedia recommender
systems, like movierecommendation, news recommendation, music recommen-dation, and …

Proposed in 2014, Generative Adversarial Networks (GAN) initiated a fresh interest in
generative modelling. They immediately achieved state-of-the-art in image synthesis, image …

Heterogeneous information network has been widely used to alleviate sparsity and cold start
problems in recommender systems since it can model rich context information in user-item …