12 datasets found

The dataset used in the paper for session-based recommendation.

The dataset used in the paper for session-based recommendation.

The dataset used in the paper for session-based recommendation.

The RETAIL ROCKET dataset is a collection of user behavioral data from a real-world e-commerce website throughout approximately 4.5 months.

The TMALL dataset is a large dataset that consists of interaction logs from the e-commerce TMALL website.

The RecSim dataset is a simulation environment for testing RL agents for Session-based Recommendation.

The dataset used in the paper is a real-world offline dataset from CIKM Cup 2016 data challenge. It contains six months of user interaction logs.

Self-supervised dual-channel attentive network for session-based social recommendation.

Collaborative graph learning for session-based recommendation.

Graph Neural Network-based models have been found to be more effective than other models because they can better model the complex relationships that exist between items.

Session-based recommendation (SBR) has gained significant attention recently and it provides recommendations solely based on information from an anonymous session.

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations.