124 datasets found

The Instacart dataset consists of ~50,000 grocery products, with the shopping records of ~200 thousand users and 3 million orders.

Precise user modeling is critical for online personalized recommen-der services. Generally, users’ interests are diverse and are not limited to a single aspect, which is...

Amazon book data set contains book reviews and metadata from Amazon. Following previous work, we regard reviews as positive samples and randomly select products not rated by a...

The production data set collected from Alibaba display advertising platform. We randomly select about 1% of 1-day samples for training and 0.1% of the next-day samples for...

The dataset used in the paper is a user-item interaction graph, where each edge represents a user-item interaction.

The dataset used in the paper is a user-item interaction graph, where each edge represents a user-item interaction.

The dataset is used for video recommendation, and it contains user-video interactions.

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 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...

The dataset used in the paper is a large online video game distribution platform. The dataset contains 2,567,538 users, 15,474 games and 7,793,069 English reviews spanning...

The dataset used in the paper for item cold start recommendation via Adversarial Variational Autoencoder Warm-up

The dataset used in the Controllable Gradient Item Retrieval paper, which consists of user-item interaction data and item-attribute relation data.

Recommender Systems FeedbackLog

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

The proposed model combines recurrent user history representation with a mixture density network, where a novel attention-based recurrent mixture density has been proposed to...

INSPIRED is a new dataset of 1,001 human-human dialogs for movie recommendation with measures for successful recommendations.

The Amazon Books dataset is a collection of user ratings for books, with each rating indicating the user's preference for the book.

KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

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.