45 datasets found

The associated task is to predict the movie rating on a 5-star scale. This dataset contains 6,040 users, 3,900 movies, and 1,000,209 ratings, i.e., rating matrix is 4.26% full.

The dataset used in the paper is MovieLens 1 Million Dataset and LDOS-CoMoDa Dataset

CiteULike is a user-article dataset, where each article has a 300-dimension tf-idf vector. XING is a user-view-job dataset where each job is described by a 2738-dimension...

The dataset used in the paper is a collection of implicit interactions gathered from various sources, including social networks and online communities.

The dataset used in the paper is a collection of explicit interactions gathered from various sources, including music websites, movie ratings, book clubs, social networks, and...

The dataset used in the paper is a collection of implicit interactions gathered from various sources, including music websites, movie ratings, book clubs, social networks, and...

Movielens-100k dataset is a network of user-movie ratings

The dataset used in the paper is the Yelp Challenge Dataset and the IMDB corpus of movie reviews.

The dataset used in the paper is a collection of explicit interactions gathered from various sources, including music websites, movie ratings, book clubs, social networks, and...

The dataset consists of data from seven participants whose response times ranged from 4 to 429 seconds. The dataset includes information about the participant, the response...

The Book-Crossing dataset is a book rating dataset that contains rating records of books by users from the Book-Crossing community.

Knowledge graphs (KGs) are collections of facts. Some well-known knowledge graphs include Freebase (Bollacker et al., 2008), Word-Net (Miller, 1995), YAGO (Suchanek et al.,...

Knowledge graphs store a wealth of knowledge from the real world into structured graphs, which consist of collections of triplets, and each triplet (h, r, t) represents that...

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is...

The Flixster dataset is a collection of user-item interactions.

The Yahoo! Music dataset is a collection of user-item interactions.

MovieLens-100K and MovieLens-1M datasets are used for performance testing of comparative experiments.

Matrix completion has been formulated as the link prediction problem on a bipartite user-item graph in recent GNN-based matrix completion methods.

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,...

Joint optimization of tree-based index and deep model for recommender systems.