Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models

The evolution of conversational recommendation systems has transitioned from rule-based and attribute-centric methods to models leveraging external knowledge graphs (KGs) such as DBpedia and ConceptNet. Early systems relied on static attribute matching, lacking natural interaction capabilities. The introduction of graph neural networks (e.g., RGCN) enabled relational modeling, but these approaches often used fixed embeddings, limiting reasoning depth. Recent advances incorporated pretrained language models (PLMs) like BERT and GPT, which improved contextual understanding but struggled with integrating structured knowledge dynamically. Existing fusion methods, such as concatenation or shallow attention, failed to fully exploit KG semantics, leading to issues like hallucination and poor personalization. The challenge remains to develop methods that can perform deep, context-aware reasoning over complex relational data while maintaining efficiency and interpretability.

Despite progress, current CRS face significant hurdles: they cannot fully leverage the relational richness of KGs for deep reasoning; they often incorporate retrieved knowledge indiscriminately, introducing noise; and they overlook latent user preferences embedded in multi-turn dialogues. These limitations hinder recommendation accuracy, reduce response diversity, and impair system robustness. Effectively filtering relevant knowledge, dynamically constructing context-aware knowledge representations, and capturing multi-turn user preferences are critical yet unresolved problems. Addressing these issues is essential for creating truly intelligent, personalized dialogue systems capable of complex reasoning and natural interaction.

This work introduces a hierarchical, dialogue-specific knowledge tree construction method, enabling structure-aware reasoning. Key innovations include: 1) Dynamic knowledge tree retrieval from KG using a RAG-style strategy, capturing relevant multi-hop relations; 2) Serialization of trees into natural language sequences, preserving structural information; 3) Integration of global KG encoding (RGCN) with local knowledge structures via contrastive learning and semantic alignment; 4) User preference extraction from multi-turn dialogues, explicitly modeling collaborative signals; 5) Soft prompt tuning within a multi-module framework, enhancing adaptability and interpretability. These innovations collectively enable deep relational reasoning, effective knowledge filtering, and personalized response generation, surpassing prior static or shallow fusion methods.

• �� Encode the entire KG with RGCN to obtain global entity embeddings. • For each dialogue, encode context with RoBERTa, then retrieve multi-hop knowledge trees for mentioned entities via relevance scoring. • Serialize trees into depth-first sequences with relation markers, encode with RoBERTa to preserve structure. • Use contrastive learning to align tree representations with entity embeddings, filtering irrelevant info. • Extract user preferences from multi-turn dialogues using self-attention, encode as preference vectors. • Combine global KG, knowledge trees, dialogue context, and preferences into prompts; feed into DialoGPT for response generation. • Optimize via multi-task loss: recommendation, dialogue quality, and preference accuracy, with staged training for stability.

Experiments utilize ReDial and INSPIRED datasets, containing multi-turn dialogues about movies. Baselines include ReDial, KBRD, KGSF, and UniCRS. Evaluation metrics cover recall@k, NDCG@k, MRR@k for recommendation, and diversity/distinct-n for response quality. Hyperparameters are tuned via grid search, with a two-stage training process: first pretraining modules, then joint fine-tuning. Ablation studies remove modules like knowledge trees or alignment to assess contribution. Results show significant improvements in recommendation metrics (up to 19.70% in MRR@10) and response diversity (up to 55.23% in Dist-2), validating the effectiveness of structured knowledge integration and user preference modeling.

PCRS-TKA achieves state-of-the-art performance, with MRR@10 reaching 0.166 on INSPIRED, outperforming baselines by nearly 20%. Response diversity metrics show a 55.23% increase in Dist-2, indicating more varied replies. Human evaluation confirms improvements in fluency, informativeness, and consistency, with scores rising by 14%. Ablation results reveal that removing knowledge trees or semantic alignment causes notable performance drops, emphasizing their importance. Overall, the model demonstrates robust multi-turn reasoning, personalized recommendations, and natural dialogue generation, validating the proposed approach’s superiority.

This framework is suited for intelligent virtual assistants, personalized recommendation engines, and customer service bots, especially where multi-turn interactions and external knowledge are critical. It can be deployed in e-commerce, entertainment, and education sectors, providing more accurate, context-aware, and explainable responses. The ability to dynamically filter and reason over structured knowledge makes it adaptable to various domains, enhancing user experience and system reliability. Future integration with multimodal data can further expand its practical utility.

The approach relies heavily on the quality and coverage of external knowledge graphs, which may limit performance in sparse or noisy KG environments. Computational complexity increases due to knowledge tree construction and contrastive learning, posing challenges for real-time deployment. The current focus on movie recommendation limits generalization; applying to other domains requires additional adaptation. Future work should address efficiency, KG quality, and cross-domain robustness to broaden applicability.