76 datasets found

Graph learning based recommender systems: A review

A survey on graph-based recommender systems

Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations

The EvalRS dataset is a music recommendation dataset used for the E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender Systems paper.

The proposed time aware cross-network recommender solution transfers auxiliary user interaction information from source networks to a target network and provides recommendations...

The dataset used in the paper is Alipay, Tmall, and Alimama. These datasets are used for click-through rate (CTR) prediction. The datasets contain user and item features, as...

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...

Taobao is a large online shopping platform where items (products) could be viewed and purchased by people online.

MovieLens 1 Million Dataset is a large-scale movie recommendation dataset, which contains 6040 users and 3706 items.

The dataset used in this paper is a collection of user-item interactions, where each interaction is represented by a user ID, item ID, and a binary indicator of whether the user...

The Movielens-IMDB and Amazon datasets are used to evaluate the proposed algorithm.

The FilmTrust dataset is a large-scale recommender system dataset, containing 35,497 user-item interaction records.

The CiaoDVD dataset is a large-scale recommender system dataset, containing 72,665 user-item interaction records.

Recommender systems predict what items a user will interact with next, based on their past interactions.

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.

A Deep, Forgetful Novelty-Seeking Movie Recommender Model

Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems.

The MovieLens-100K and MovieLens-ml-latest-small datasets are used to evaluate the effectiveness of the proposed detection method.

The dataset is a two-mode tensor depicting binary (user, anime) preferences. The tensor contains 1, 300, 160 observed entries, of size 25, 838 × 4, 066.

A dataset of movie ratings, a dataset of anime ratings.