62 datasets found

The Yelp dataset challenge contains reviews and images of restaurants, with the goal of recommending images for each review.

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...

The Flixster dataset is a collection of user-item interactions.

The Yahoo! Music dataset is a collection of user-item interactions.

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

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,...

Joint optimization of tree-based index and deep model for recommender systems.

A concept-aware denoising graph neural network for micro-video recommendation

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 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 this paper is a user-item click matrix with item content data and item style labels.

This dataset is used for the experiment of learning to rank and user fairness in recommender systems.

This dataset is used for the experiment of improving the utility of recommender systems for non-mainstream users.

This dataset is used for the experiment of mitigating mainstream bias in recommender systems.

This dataset is used for the experiment of metric optimization for ranking-based recommendation systems.

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

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

The dataset is used for click-through rate (CTR) prediction task in recommender systems.