13 datasets found

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

The dataset used in the paper is a Netflix dataset, which is a large-scale matrix factorization problem.

The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4):1–19, 2015.

The dataset used in this paper is a large-scale dataset for implicit feedback, consisting of user-item interactions, where each interaction is a pair of a user and an item.

The dataset used in the paper is a user-item bipartite graph, where each user and item is represented as a node, and the edges between them represent the interactions between...

The Netflix Prize dataset is a large-scale movie recommendation dataset. It contains 1000 user-item interaction records, with each record representing a user's rating of a movie.

The MovieLens Boxoffice dataset is a large-scale movie recommendation dataset. It contains 99326 user-item interaction records, with each record representing a user's rating of...

The MovieLens 20m Light dataset is a large-scale movie recommendation dataset. It contains 20 million user-item interaction records, with each record representing a user's...

The MovieLens dataset contains questions answerable using Wikidata as the knowledge graph, focusing on questions with a single entity and relation.

The dataset is a movie review dataset with five types of nodes (movie, director, tag, writer, and user) and four types of edges (movie-director relation, movie-tag relation,...

The Movielens-100k dataset is a large-scale movie recommendation dataset.

The Movie dataset is a graph dataset constructed from the Movielens-2k dataset. The Movielens-2k dataset contains movies information such as actors, genres, and tags...