62 datasets found

The dataset used in this paper is the MovieLens 1M dataset, which contains a 1M 1-5 star ratings by 6,040 users for 3,952 movies.

A Deep, Forgetful Novelty-Seeking Movie Recommender Model

Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems.

The MovieLens-100K and MovieLens-ml-latest-small datasets are used to evaluate the effectiveness of the proposed detection method.

The dataset is a two-mode tensor depicting binary (user, anime) preferences. The tensor contains 1, 300, 160 observed entries, of size 25, 838 × 4, 066.

A dataset of movie ratings, a dataset of anime ratings.

A First Look at Selection Bias in Preference Elicitation for Recommendation

The dataset used in this research work is a company's content management system (CMS) dataset. It has two significant entities: posts and users, which consists of 250 users, and...

The bAbI Movie Dialog dataset is a synthetic dataset containing elementary tasks such as selecting an answer between one or more candidate facts, answering yes/no questions,...

The Amazon dataset is used to evaluate the performance of the proposed approach. It consists of 2000 users, 1500 items, 86690 reviews, 7219 number ratings, 3.6113 average number...

A simplified simulated dataset showing varying user preferences over time and how this can be evaluated with static and dynamic learning algorithms.

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions.

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions.

Offline evaluations of recommender systems attempt to estimate users’ satisfaction with recommendations using static data from prior user interactions.

The dataset used for term clustering to build a modular ontology according to core ontology from domain-specific text.

A dataset for interactive recommender systems, used to evaluate the proposed Tree-structured Policy Gradient Recommendation (TPGR) framework.

The dataset used in this paper is a collection of research papers, and the authors propose a scholarly paper recommendation system.

The dataset used in this paper is a collection of research papers, and the authors propose an interactive research paper recommender system.

The dataset used in the paper is a large-scale recommendation system dataset, which includes user profiles and user history behavior sequences.

The Epinions dataset is a large-scale opinion mining dataset. It contains 1 million user-item interactions and is widely used for evaluating the performance of recommender systems.