Items Proxy Bridging: Enabling Frictionless Critiquing in Knowledge Graph Recommendations

2025-09-30

AI Summary

Existing critique-based recommendation methods rely on dedicated models to construct user–keyword mappings, suffering from poor generalizability and catastrophic forgetting during multi-turn critique due to continuous parameter updates. Method: We propose a generic item-proxy mechanism that automatically translates user critiques on keywords into optimization objectives over items—requiring no architectural modification to baseline models and enabling plug-and-play integration into mainstream collaborative filtering frameworks. Furthermore, we design a forgetting-robust regularization strategy to mitigate performance degradation induced by multi-step parameter updates. Contribution/Results: In knowledge graph–enhanced recommendation scenarios, our approach enables real-time, frictionless multi-turn interactive optimization. Extensive experiments across multiple benchmark datasets demonstrate significant improvements in both recommendation stability and accuracy, while effectively alleviating catastrophic forgetting.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Modern recommender systems place great inclination towards facilitating user experience, as more applications enabling users to critique and then refine recommendations immediately. Considering the real-time requirements, critique-able recommender systems typically straight modify the model parameters and update the recommend list through analyzing the user critiquing keyphrases in the inference phase. Current critiquing methods require first constructing a specially designated model which establish direct correlations between users and keyphrases during the training phase allowing for innovative recommendations upon the critiquing,restricting the applicable scenarios. Additionally, all these approaches ignore the catastrophic forgetting problem, where the cumulative changes in parameters during continuous multi-step critiquing may lead to a collapse in model performance. Thus, We conceptualize a proxy bridging users and keyphrases, proposing a streamlined yet potent Items Proxy Generic Critiquing Framework (IPGC) framework, which can serve as a universal plugin for most knowledge graph recommender models based on collaborative filtering (CF) strategies. IPGC provides a new paradigm for frictionless integration of critique mechanisms to enable iterative recommendation refinement in mainstream recommendation scenarios. IPGC describes the items proxy mechanism for transforming the critiquing optimization objective of user-keyphrase pairs into user-item pairs, adapting it for general CF recommender models without the necessity of specifically designed user-keyphrase correlation module. Furthermore, an anti-forgetting regularizer is introduced in order to efficiently mitigate the catastrophic forgetting problem of the model as a prior for critiquing optimization.

Problem

• Enables frictionless critiquing in knowledge graph recommendations
• Transforms critiquing optimization from user-keyphrase to user-item pairs
• Mitigates catastrophic forgetting during continuous multi-step critiquing

Innovation

• Items proxy bridges users and keyphrases for critiquing
• Universal plugin for knowledge graph recommender models
• Anti-forgetting regularizer mitigates catastrophic forgetting problem