124 datasets found

The Yelp Dataset contains 1.6M reviews and 500K tips by 366K users for 61K businesses; 481K business attributes, such as hours, parking availability, ambience; and check-ins for...

The dataset used in the paper is a large-scale recommendation system dataset, where each item or user may correspond to a separate bandit arm.

FashionCLIP is a large model built for fashion, used to introduce GradREC, a new type of recommendation experience.

A novel Markov decision process (MDP)-based re-ranking model for final-stage recommendation, called Panel-MDP.

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

Office dataset consists of 31 categories and contains around 4,700 images across three domains, namely Amazon(A), DSLR(D) and Webcam(W).

The dataset used in this paper for recommendation system, it is a bipartite graph of user-item interactions.

The dataset used in the paper is a user-item bipartite graph, where each user and item is represented as a node, and the edges between them represent the interactions between...

The dataset used in this paper is a high-rating movie recommendation system. The objective of the system is to recommend high-rating movies to users, but the ratings for the...

The Netflix Prize dataset is a large-scale movie recommendation dataset. It contains 1000 user-item interaction records, with each record representing a user's rating of a movie.

The MovieLens Boxoffice dataset is a large-scale movie recommendation dataset. It contains 99326 user-item interaction records, with each record representing a user's rating of...

The MovieLens 20m Light dataset is a large-scale movie recommendation dataset. It contains 20 million user-item interaction records, with each record representing a user's...

Multi-behavior recommendation datasets

Multi-behavior recommendation datasets

Multi-behavior recommendation datasets

Multi-behavior recommendation datasets

Multi-behavior recommendation datasets

Multi-behavior recommendation datasets

Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is...

MovieLens-100K and MovieLens-1M datasets are used for performance testing of comparative experiments.