A review of modern recommender systems using generative models (gen-recsys)
Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …
source. However, deep generative models now have the capability to model and sample …
A survey on adversarial recommender systems: from attack/defense strategies to generative adversarial networks
Latent-factor models (LFM) based on collaborative filtering (CF), such as matrix factorization
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
(MF) and deep CF methods, are widely used in modern recommender systems (RS) due to …
Elliot: A comprehensive and rigorous framework for reproducible recommender systems evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice
problem and provide accurate and tailored recommendations. However, the impressive …
problem and provide accurate and tailored recommendations. However, the impressive …
Generative adversarial framework for cold-start item recommendation
The cold-start problem has been a long-standing issue in recommendation. Embedding-
based recommendation models provide recommendations by learning embeddings for each …
based recommendation models provide recommendations by learning embeddings for each …
Enhancing social recommendation with adversarial graph convolutional networks
Social recommender systems are expected to improve recommendation quality by
incorporating social information when there is little user-item interaction data. However …
incorporating social information when there is little user-item interaction data. However …
Bootstrapping user and item representations for one-class collaborative filtering
The goal of one-class collaborative filtering (OCCF) is to identify the user-item pairs that are
positively-related but have not been interacted yet, where only a small portion of positive …
positively-related but have not been interacted yet, where only a small portion of positive …
Enhancing collaborative filtering with generative augmentation
Collaborative filtering (CF) has become one of the most popular and widely used methods in
recommender systems, but its performance degrades sharply for users with rare interaction …
recommender systems, but its performance degrades sharply for users with rare interaction …
Adversarial and contrastive variational autoencoder for sequential recommendation
Sequential recommendation as an emerging topic has attracted increasing attention due to
its important practical significance. Models based on deep learning and attention …
its important practical significance. Models based on deep learning and attention …
Reinforcement learning over sentiment-augmented knowledge graphs towards accurate and explainable recommendation
Explainable recommendation has gained great attention in recent years. A lot of work in this
research line has chosen to use the knowledge graphs (KG) where relations between …
research line has chosen to use the knowledge graphs (KG) where relations between …
[PDF][PDF] Reinforced Negative Sampling for Recommendation with Exposure Data.
In implicit feedback-based recommender systems, user exposure data, which record
whether or not a recommended item has been interacted by a user, provide an important …
whether or not a recommended item has been interacted by a user, provide an important …