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

• URL: http://arxiv.org/abs/2511.12579v1
• Date: Sun, 16 Nov 2025 12:44:55 GMT
• Title: Enhancing Conversational Recommender Systems with Tree-Structured Knowledge and Pretrained Language Models
• Authors: Yongwen Ren, Chao Wang, Peng Du, Chuan Qin, Dazhong Shen, Hui Xiong,
• Abstract summary: PCRS-TKA is a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs.<n>It constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning.<n>It consistently outperforms all baselines in both recommendation and conversational quality.
• Score: 24.415830340607783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in pretrained language models (PLMs) have significantly improved conversational recommender systems (CRS), enabling more fluent and context-aware interactions. To further enhance accuracy and mitigate hallucination, many methods integrate PLMs with knowledge graphs (KGs), but face key challenges: failing to fully exploit PLM reasoning over graph relationships, indiscriminately incorporating retrieved knowledge without context filtering, and neglecting collaborative preferences in multi-turn dialogues. To this end, we propose PCRS-TKA, a prompt-based framework employing retrieval-augmented generation to integrate PLMs with KGs. PCRS-TKA constructs dialogue-specific knowledge trees from KGs and serializes them into texts, enabling structure-aware reasoning while capturing rich entity semantics. Our approach selectively filters context-relevant knowledge and explicitly models collaborative preferences using specialized supervision signals. A semantic alignment module harmonizes heterogeneous inputs, reducing noise and enhancing accuracy. Extensive experiments demonstrate that PCRS-TKA consistently outperforms all baselines in both recommendation and conversational quality.

Related papers

• STEP: Stepwise Curriculum Learning for Context-Knowledge Fusion in Conversational Recommendation [18.833994388759326]
  We introduce STEP, a conversational recommender centered on pre-trained language models.<n> STEP combines curriculum-guided context-knowledge fusion with lightweight task-specific prompt tuning.<n> Experimental results show that STEP outperforms mainstream methods in the precision of recommendation and dialogue quality in two public datasets.
  arXiv  Detail & Related papers  (2025-08-14T14:08:21Z)
• Graph Retrieval-Augmented LLM for Conversational Recommendation Systems [52.35491420330534]
  G-CRS (Graph Retrieval-Augmented Large Language Model for Conversational Recommender Systems) is a training-free framework that combines graph retrieval-augmented generation and in-context learning.<n>G-CRS achieves superior recommendation performance compared to existing methods without requiring task-specific training.
  arXiv  Detail & Related papers  (2025-03-09T03:56:22Z)
• Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations [58.61021630938566]
  Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues.<n>Current CRSs often leverage knowledge graphs (KGs) or language models to extract and represent user preferences as latent vectors, which limits their explainability.<n>We propose a plug-and-play framework that synergizes LLMs and KGs to reason over user preferences, enhancing the performance and explainability of existing CRSs.
  arXiv  Detail & Related papers  (2024-11-16T11:47:21Z)
• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation [48.70062671767362]
  We propose the Variational Reasoning over Incomplete KGs Conversational Recommender (VRICR) Our key idea is to incorporate the large dialogue corpus naturally accompanied with CRSs to enhance the incomplete KGs. We also denote the dialogue-specific subgraphs of KGs as latent variables with categorical priors for adaptive knowledge graphs.
  arXiv  Detail & Related papers  (2022-12-22T17:02:21Z)
• Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information [42.88448098873448]
  Conversational recommendation systems (CRS) engage with users by inferring user preferences from dialog history. Previous CRSs use knowledge graph (KG) based recommendation modules and integrate KG with language models for response generation. We propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder.
  arXiv  Detail & Related papers  (2021-12-15T14:12:48Z)
• Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph [35.033130888779226]
  We present a pre-trained language model (PLM) based framework called RID conversational recommender system (CRS) RID significantly outperforms the state-of-the-art methods on both evaluations of dialogue and recommendation.
  arXiv  Detail & Related papers  (2021-10-14T15:49:48Z)
• Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion [77.21442487537139]
  Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. First, the conversation data itself lacks of sufficient contextual information for accurately understanding users' preference. Second, there is a semantic gap between natural language expression and item-level user preference.
  arXiv  Detail & Related papers  (2020-07-08T11:14:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.