36 datasets found

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.

The dataset used in the paper is a series of datasets introduced in [46], comprising large corpora of product reviews crawled from Amazon.com. Top-level product categories on...

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 Amazon-Book dataset contains user-item interaction data, which is used to evaluate the performance of recommender systems.

The Gowalla dataset contains user-item interaction data, which is used to evaluate the performance of recommender systems.

The dataset used in this paper is the MovieLens 20M dataset, which contains ratings from 92,032 users on 20,000 movies.