13 datasets found

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...

A dataset for interactive recommender systems, used to evaluate the proposed Tree-structured Policy Gradient Recommendation (TPGR) framework.

The Epinions dataset is a large-scale opinion mining dataset. It contains 1 million user-item interactions and is widely used for evaluating the performance of recommender systems.

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.

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

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...

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

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,...

A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K...

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