Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
Causal inference for recommender systems
The task of recommender systems is classically framed as a prediction of users' preferences
and users' ratings. However, its spirit is to answer a counterfactual question:“What would the …
and users' ratings. However, its spirit is to answer a counterfactual question:“What would the …
Dropoutnet: Addressing cold start in recommender systems
Latent models have become the default choice for recommender systems due to their
performance and scalability. However, research in this area has primarily focused on …
performance and scalability. However, research in this area has primarily focused on …
Ask the GRU Multi-task Learning for Deep Text Recommendations
In a variety of application domains the content to be recommended to users is associated
with text. This includes research papers, movies with associated plot summaries, news …
with text. This includes research papers, movies with associated plot summaries, news …
Modeling user exposure in recommendation
Collaborative filtering analyzes user preferences for items (eg, books, movies, restaurants,
academic papers) by exploiting the similarity patterns across users. In implicit feedback …
academic papers) by exploiting the similarity patterns across users. In implicit feedback …
Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence
Matrix factorization (MF) models and their extensions are standard in modern recommender
systems. MF models decompose the observed user-item interaction matrix into user and …
systems. MF models decompose the observed user-item interaction matrix into user and …
A neural autoregressive approach to collaborative filtering
Y Zheng, B Tang, W Ding… - … Conference on Machine …, 2016 - proceedings.mlr.press
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative
filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF …
filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF …
On sampling strategies for neural network-based collaborative filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …
recommendation algorithms that can incorporate both (1) user-item interaction information …
Supervised word mover's distance
Accurately measuring the similarity between text documents lies at the core of many real
world applications of machine learning. These include web-search ranking, document …
world applications of machine learning. These include web-search ranking, document …
[PDF][PDF] Scalable Recommendation with Hierarchical Poisson Factorization.
We develop hierarchical Poisson matrix factorization (HPF), a novel method for providing
users with high quality recommendations based on implicit feedback, such as views, clicks …
users with high quality recommendations based on implicit feedback, such as views, clicks …