18 datasets found

The Beauty dataset is a product review dataset crawled from Amazon. The data is split into separate datasets by the top-level product category.

The dataset used in this paper is a commercial search engine query log, containing past queries and sessions of users.

A dataset of search logs collected from a commercial search engine in the period of 11/22/2020 ∼ 11/28/2020.

A dataset of search logs collected from a commercial search engine in the period of 11/22/2020 ∼ 11/28/2020.

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.

Location prediction forecasts a user’s location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces,...

The dataset contains click statistics from 85 participants over the course of 4 months, from September 2023 until January 2024.

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 is a collection of user queries and clicked images from Bing search logs.

The Airbnb dataset is a large dataset containing information about apartments for rent in Chicago. The dataset includes features such as neighborhood, number of beds, amenities,...

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 Yahoo dataset used for training and testing the proposed model, containing leaked passwords.

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

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

The dataset used in the paper is a set of 3, 2, 0, and 2, representing the number of users, arcs, and Karma levels.

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

This dataset is used to evaluate the proposed Disentangled Graph Collaborative Filtering (DGCF) model.

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