124 datasets found

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  • MovieLens dataset

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

  • MovieLens

    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,...

  • Gowalla, Yelp, and ML-1M

    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...

  • End-to-End User Behavior Retrieval in Click-Through Rate Prediction Model

    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.

  • Neural Collaborative Filtering

    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.

  • VideoRerank Dataset

    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...

  • Avito 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

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

  • ML-20M

    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...

  • Amazon

    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...

  • Yelp2018, Amazon-book, and Alibaba-iFashion

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

  • Amazon review dataset

    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...

  • Self-Attentive Sequential Recommendation

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

  • Music, Book, Amazon, and Yelp

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

  • Amazon-Book

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

  • Yelp2018*

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

  • Gowalla

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

  • Synthetic Dataset

    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

    Fashion Product Images Dataset containing eyewear, footwear, and bags

  • IJCAI-Contest Dataset

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