-
Graph learning based recommender systems: A review
Graph learning based recommender systems: A review -
A survey on graph-based recommender systems
A survey on graph-based recommender systems -
A deep framework for cross-domain and cross-system recommendations
A deep framework for cross-domain and cross-system recommendations -
A graph-based framework for cross-domain and cross-system recommendations
A graph-based framework for cross-domain and cross-system recommendations -
A unified framework for cross-domain and cross-system recommendations
A unified framework for cross-domain and cross-system recommendations -
Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization...
Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations -
Exploring the use of Time-Dependent Cross-Network Information for Personalize...
The proposed time aware cross-network recommender solution transfers auxiliary user interaction information from source networks to a target network and provides recommendations... -
Bandana: Using Non-volatile Memory for Storing Deep Learning Models
Typical large-scale recommender systems use deep learning models that are stored on a large amount of DRAM. These models often rely on embeddings, which consume most of the... -
MovieLens 1 Million Dataset
MovieLens 1 Million Dataset is a large-scale movie recommendation dataset, which contains 6040 users and 3706 items. -
MovieLens 1M dataset
The dataset used in this paper is the MovieLens 1M dataset, which contains a 1M 1-5 star ratings by 6,040 users for 3,952 movies. -
MovieLens-100K and MovieLens-ml-latest-small
The MovieLens-100K and MovieLens-ml-latest-small datasets are used to evaluate the effectiveness of the proposed detection method. -
MovieLens1M, Anime
A dataset of movie ratings, a dataset of anime ratings. -
Amazon dataset
The Amazon dataset is used to evaluate the performance of the proposed approach. It consists of 2000 users, 1500 items, 86690 reviews, 7219 number ratings, 3.6113 average number... -
DiffRec, L-DiffRec, and T-DiffRec datasets
DiffRec, L-DiffRec, and T-DiffRec datasets. -
Diffusion Recommender Model
Diffusion Recommender Model, which infers users’ interaction probabilities in a denoising manner. -
MovieLens100K and MovieLens1M datasets
The MovieLens100K and MovieLens1M datasets are used to evaluate the proposed method.
