Session-Based Recommendations
75 papers with code • 7 benchmarks • 3 datasets
Recommendation based on a sequence of events. e.g. next item prediction
Benchmarks
These leaderboards are used to track progress in Session-Based Recommendations
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Libraries
Use these libraries to find Session-Based Recommendations models and implementations
Gowalla
OTTO Recommender Systems Dataset
Latest papers with no code
Re2LLM: Reflective Reinforcement Large Language Model for Session-based Recommendation
However, the former methods struggle with optimal prompts to elicit the correct reasoning of LLMs due to the lack of task-specific feedback, leading to unsatisfactory recommendations.
Explainable Session-based Recommendation via Path Reasoning
This paper explores providing explainability for session-based recommendation (SR) by path reasoning.
Side Information-Driven Session-based Recommendation: A Survey
Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance.
Integrating Large Language Models with Graphical Session-Based Recommendation
SBR has been primarily dominated by Graph Neural Networks, which have achieved many successful outcomes due to their ability to capture both the implicit and explicit relationships between adjacent behaviors.
LLM4SBR: A Lightweight and Effective Framework for Integrating Large Language Models in Session-based Recommendation
However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR.
Performance Comparison of Session-based Recommendation Algorithms based on GNNs
In session-based recommendation settings, a recommender system has to base its suggestions on the user interactions that are ob served in an ongoing session.
Exploring Popularity Bias in Session-based Recommendation
Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback.
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions.
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation
CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN.
TempGNN: Temporal Graph Neural Networks for Dynamic Session-Based Recommendations
Session-based recommendations which predict the next action by understanding a user's interaction behavior with items within a relatively short ongoing session have recently gained increasing popularity.
