Explainable Session-based Recommendation via Path Reasoning: Enhancing Accuracy and Explainability

Keskeiset käsitteet

The author proposes a generalized hierarchical reinforcement learning framework, PR4SR, to enhance the explainability of session-based recommendation models by incorporating path reasoning. This approach aims to improve accuracy while providing transparent and understandable recommendations.

Tiivistelmä

The paper introduces PR4SR, a novel framework that combines hierarchical reinforcement learning with path reasoning to enhance the explainability of session-based recommendation models. By selecting important items from sessions and exploring paths in knowledge graphs, PR4SR improves both accuracy and transparency in recommendations. The study compares PR4SR with existing frameworks on four datasets, demonstrating its effectiveness in improving recommendation accuracy and model explainability.
The research addresses the limitations of current session-based recommendation models by focusing on explainability through path reasoning. By incorporating features from images into knowledge graphs and designing reward mechanisms for skip behaviors in sequential patterns, PR4SR offers a comprehensive solution for enhancing the completeness and diversity of explanations in recommendations.
Through extensive experiments on real-world datasets, the study showcases the superior performance of PR4SR compared to traditional SR models and existing explainable frameworks. The results highlight the potential of hierarchical reinforcement learning combined with path reasoning to revolutionize session-based recommendation systems.

Tilastot

The average length of sessions is 2.96 for Beauty dataset, 2.75 for Cellphones dataset, 2.014 for Baby dataset.
The number of entities varies across datasets: Beauty (204,007), Cellphones (136,811), Baby (97,851), Douban-movie (77,760).
Various relations are present in the datasets: Amazon datasets have over 3 million relations each while Douban-movie has 326,064 relations.

Lainaukset

"The main problem lies in previous research only considering how to perform path reasoning in knowledge graphs."
"PR4SR generalizes well and can be combined with existing unexplainable SR models to accomplish both recommendation and explainability tasks simultaneously."

Tärkeimmät oivallukset

Explainable Session-based Recommendation via Path Reasoning

by Yang Cao,Shu... klo arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.00832.pdf

Syvällisempiä Kysymyksiä

How can hierarchical reinforcement learning be applied to other domains beyond session-based recommendations

Hierarchical reinforcement learning can be applied to other domains beyond session-based recommendations by adapting the framework to suit the specific characteristics and requirements of different domains. For example, in healthcare, hierarchical reinforcement learning could be used to optimize treatment plans for patients based on their medical history and current condition. The session-level agent could select relevant patient data as the starting point for path reasoning, while the path-level agent explores possible treatment paths in a knowledge graph representing medical interventions. By incorporating domain-specific knowledge and reward mechanisms, hierarchical reinforcement learning could help improve personalized healthcare recommendations.

What are potential drawbacks or challenges associated with incorporating image feature information into knowledge graphs

Incorporating image feature information into knowledge graphs may present several drawbacks or challenges. One challenge is ensuring the accuracy and relevance of the extracted features from images. If the image recognition algorithms are not robust or if irrelevant features are extracted, it could lead to noisy or misleading information being added to the knowledge graph. Additionally, managing large amounts of image data and integrating them into existing knowledge graphs can be computationally intensive and resource-intensive. Another drawback is that images may contain subjective or context-dependent information that may not always align with structured data in a knowledge graph, leading to potential inconsistencies or inaccuracies in representation.

How might the concept of "explainable paths" impact user trust and engagement with recommendation systems

The concept of "explainable paths" can have a significant impact on user trust and engagement with recommendation systems by providing transparency and insight into how recommendations are generated. When users understand why certain items are recommended based on explainable paths through hierarchical reinforcement learning frameworks like PR4SR, they are more likely to trust the system's suggestions. This increased transparency can enhance user confidence in decision-making processes driven by AI algorithms.
Furthermore, explainable paths can also increase user engagement by making recommendations more personalized and relevant to individual preferences. Users who receive explanations for why certain items were recommended are more likely to interact with those recommendations positively since they align better with their interests.
Overall, incorporating explainable paths into recommendation systems not only improves user understanding but also fosters trustworthiness and engagement with AI-driven suggestions.