124 datasets found

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,...

The dataset used in this paper is Gowalla, Yelp, and ML-1M. Gowalla is a social media platform with 210,537 users and 100,000 items. Yelp is a review website with 1,000,209...

Click-Through Rate (CTR) prediction is one of the core tasks in recommender systems (RS). It predicts a personalized click probability for each user-item pair.

The dataset is used for neural collaborative filtering, which is a type of collaborative filtering that uses neural networks to learn the relationships between users and items.

The VideoRerank dataset is derived from Kuaishou, a widely used short-video application with a user base of over 300 million daily active users. Each sample in the dataset...

The Avito dataset is a publicly available collection of user search logs. Each sample represents a search page containing multiple ads, forming a really impressive list with...

MovieLens-1M and Amazon Cell Phone datasets are used for evaluation.

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 dataset used in the paper is Yelp2018, Amazon-book, and Alibaba-iFashion. These datasets are used for top-k recommendation tasks.

The Amazon review dataset is used for multi-source domain adaptation. It contains review texts and ratings of bought products. Products are grouped into categories. Following...

The Steam-2M dataset is a large-scale dataset for game ratings.

The dataset used in the paper is Music, Book, Amazon, and Yelp. The dataset contains user-item interactions and knowledge graph information.

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.

The dataset used in this work is a custom synthetic dataset generated using the liquid-dsp library, containing 600000 examples of each of 13.8 million examples, with SNRs...

Fashion Product Images Dataset containing eyewear, footwear, and bags

The IJCAI-Contest dataset is a real-world dataset with 17,435 users, 31,882 items, and 35,920 interactions.