124 datasets found

The dataset used in this paper is a collection of user interaction data from the Xianyu homepage recommendation system.

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 used in the paper is MovieLens-1M, Foursquare, and Yelp2018. These datasets are used for top-k recommendation task.

The dataset contains hundreds of millions of user records. Each one is a sequence of (itemId, pageId, time) tuples, recording the context under which a recommended item consumed...

Last.FM is a dataset containing musician listening information from a set of 2 thousand users from Last.fm online music system.

The dataset used in this paper is a knowledge graph of tourist attractions on Socotra Island-Yemen, containing 1500 scenic spots, 2229 tourists, and 6091 score records.

The Neural Collaborative Filtering (NCF) dataset is a collection of user-item interaction data.

The BookCrossing dataset is a collection of user-item interaction data.

TikTok 2 and WechatMoments are real-world datasets used for evaluating the proposed CrossDistil framework.

Yelp-2018 dataset is a real-world dataset for recommender systems, containing user-item interactions.

Amazon-Beauty, Amazon-Book, Yelp2021 datasets are used for sign-aware graph recommendation. The datasets are used to evaluate the performance of the proposed LSGRec model.

The dataset used in the paper is Amazon Books, Amazon Toys, and Retailrocket.

This dataset contains the pickup, drop-off timestamps and pickup, drop-off locations of taxis in New York city from 1st Jan 2019 to 28th Feb 2019.

The dataset used in the paper is MovieLens-1M and Tmall.

The Amazon Reviews dataset is used to predict the usefulness of Amazon reviews using off-the-shelf argumentation mining.

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.

The dataset used in the paper is a large-scale graph dataset, consisting of users and shows with multi-attribute edges. The graph is constructed by selecting user IDs and side...

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 dataset used in the paper is MovieLens-1M, MovieLens-10M, and BookCrossing.

The dataset used in the paper is Taobao2014, Taobao2015, Alibaba and Amazon. The dataset is used for implicit recommendation and contains user-item interactions.