Reinforced Negative Sampling over Knowledge Graph for Recommendation (2003.05753v1)

Abstract: Properly handling missing data is a fundamental challenge in recommendation. Most present works perform negative sampling from unobserved data to supply the training of recommender models with negative signals. Nevertheless, existing negative sampling strategies, either static or adaptive ones, are insufficient to yield high-quality negative samples --- both informative to model training and reflective of user real needs. In this work, we hypothesize that item knowledge graph (KG), which provides rich relations among items and KG entities, could be useful to infer informative and factual negative samples. Towards this end, we develop a new negative sampling model, Knowledge Graph Policy Network (KGPolicy), which works as a reinforcement learning agent to explore high-quality negatives. Specifically, by conducting our designed exploration operations, it navigates from the target positive interaction, adaptively receives knowledge-aware negative signals, and ultimately yields a potential negative item to train the recommender. We tested on a matrix factorization (MF) model equipped with KGPolicy, and it achieves significant improvements over both state-of-the-art sampling methods like DNS and IRGAN, and KG-enhanced recommender models like KGAT. Further analyses from different angles provide insights of knowledge-aware sampling. We release the codes and datasets at https://github.com/xiangwang1223/kgpolicy.

• The paper introduces KGPolicy, a reinforcement learning model that refines negative sampling by strategically exploring knowledge graphs.
• It extracts high-order relationships to generate informative and factual negative samples that optimize model training.
• Experiments on Amazon-book, Last-FM, and Yelp2018 datasets demonstrate significant improvements over traditional sampling methods.

Insights into Reinforced Negative Sampling over Knowledge Graphs for Recommendation

The presented paper advances the discussion within recommender systems by addressing the crucial challenge of negative sampling in the context of implicit feedback data. It proposes a novel negative sampling approach specifically structured around the use of knowledge graphs (KG) to improve the identification of high-quality negative samples. The methodology is anchored in a reinforcement learning model, termed the Knowledge Graph Policy Network (KGPolicy), which operates as an agent to strategically explore potential pathways within the knowledge graph and yield informative and factual negative instances to refine recommender models.

Methodological Innovations

The research leverages the structural properties of knowledge graphs, extracting high-order relationships between items and their related entities, effectively utilizing the vast unobserved data in implicit feedback scenarios. The paper specifies that high-quality negative samples must meet two criteria: they should be informative, meaning they prompt significant parameter updates during training, and factual, implying they align with true user disinterest.

KGPolicy distinguishes itself with a dynamic sampling process that is adaptable and sensitive to user-specific interactions. Implemented via a reinforcement learning framework, it evaluates potential negative items through designed exploration operations via the knowledge graph. This process involves defining exploratory paths rooted in the positive user-item interaction nodes and navigating through KG entities to identify authentic negative samples.

Numerical Performance and Analysis

Experiments conducted across three large-scale datasets—Amazon-book, Last-FM, and Yelp2018—demonstrate the method's superiority over existing static and dynamic negative sampling methods, such as Random Negative Sampling (RNS), Dynamic Negative Sampling (DNS), and adversarial models including IRGAN and AdvIR. Notably, the method shows remarkable improvement especially within contextually rich datasets like Yelp2018.

Implications for Theory and Practice

The implications of this work address both theoretical and practical facets of recommendation systems. Theoretically, it proposes a fresh perspective on negative sampling, effectively marrying knowledge graph comprehension with the learning mechanisms of recommender models. Practically, it provides a pathway for improving recommendation accuracy by rigorously mining negative signals amidst implicit feedback, a frequent challenge in real-world applications. This has direct implications for designing systems in e-commerce, content platforms, and other domains where recommendations are a key interface for user interaction.

Future Directions in AI and Recommender Systems

Future developments may see enhanced focus on integrating additional contextual subtleties and external variables into the negative sampling mechanisms, such as temporal dynamics or social behavior contexts. Further exploration into hybrid models that unify the presented negative sampling strategies with advanced user preference modeling techniques could lead to more granular and dynamic recommendation insights. Specifically, extending the capabilities of KGPolicy to handle explicitly negative user experiences and feedback—thus directly capturing genuine dislikes—could significantly bolster the explanatory power of recommendation models and align them more closely with user needs and expectations.

In conclusion, this paper sets a precedent for the reinvention of negative sampling strategies in recommendation systems, reinforcing the utility of knowledge graphs and advancing the state of personalized content delivery. The approaches and insights from this paper are likely to influence future AI research areas, prompting additional inquiries and innovations within the field of recommenders.