C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
Proposed C2-CRS employs coarse-to-fine contrastive learning to fuse multi-type external data for improved conversational recommendation.
Key Findings
Methodology
The C2-CRS framework introduces a hierarchical contrastive learning approach that aligns multi-grained semantic units across heterogeneous data sources. It consists of two stages: the coarse stage aligns global semantic representations using contrastive loss (InfoNCE) with Transformer and R-GCN encodings, promoting global semantic consistency across modalities. The fine stage focuses on entity, word, and sentence-level matching, further refining the alignment through triplet-based contrastive loss. The model jointly optimizes these stages, enabling effective multi-source fusion. This design supports scalable extension to additional external data types, enhancing user preference modeling in CRS.
Key Results
- On Redial and DuRecDial datasets, C2-CRS outperformed baselines with a 8.5% increase in Recall@10 and a 6.2% boost in BLEU-4 scores, demonstrating superior recommendation and response quality. Ablation studies confirmed that removing either coarse or fine contrastive stages reduced performance by approximately 4%, indicating their complementary roles. The model effectively integrated knowledge graphs and reviews, capturing nuanced user preferences, and maintained robustness under data scarcity and noise conditions.
- The experiments validated that multi-granular semantic alignment significantly improves the system’s ability to understand user intent and preferences, leading to more accurate recommendations and more natural dialogues. The approach showed consistent gains across different external data types, confirming its generalizability and scalability.
- Furthermore, the model maintained high performance in scenarios with limited labeled data, indicating strong potential for real-world deployment in dynamic environments.
Significance
This work advances the state-of-the-art in multi-source data fusion for CRS, addressing the long-standing challenge of semantic heterogeneity. By systematically aligning multi-granular semantic units, it enhances the system’s interpretability and recommendation accuracy. The framework’s extensibility to additional data types and its robustness under noisy conditions make it highly relevant for industry applications such as personalized e-commerce, intelligent customer support, and content recommendation. The research bridges the gap between heterogeneous data representations, paving the way for more intelligent, context-aware conversational agents that can better understand complex user needs.
Technical Contribution
The core technical innovation lies in the hierarchical contrastive learning framework that enables multi-granular semantic alignment across heterogeneous data sources. Unlike prior works that treat data fusion as simple concatenation or shallow embedding, this approach explicitly models the semantic relationships at both global and entity levels, leveraging Transformer and R-GCN encodings. The joint optimization of coarse and fine contrastive objectives ensures comprehensive semantic alignment, facilitating scalable extension to additional modalities. The method offers theoretical guarantees of semantic consistency and practical improvements in recommendation and dialogue tasks, representing a significant step forward in multi-source data integration.
Novelty
This is the first work to systematically apply a coarse-to-fine contrastive learning paradigm for multi-type external data fusion in CRS. Unlike existing methods that focus on single-modal or shallow fusion, this approach explicitly models multi-granular semantic relationships, enabling deep alignment across heterogeneous data. Its hierarchical design and entity-word-sentence matching mechanism set it apart, providing a new paradigm for multi-source semantic integration in conversational AI.
Limitations
- The model's training complexity and computational cost are high due to multi-stage contrastive objectives and large-scale encodings, limiting real-time deployment in resource-constrained environments.
- Dependence on high-quality external data, such as complete knowledge graphs and reviews, may hinder performance when数据缺失或质量较差时表现不佳。
- 在动态场景中，模型对新用户或新物品的适应性仍需增强，未来需引入在线学习和自适应机制。
Future Work
未来将探索多源数据的自适应选择和动态更新机制，提升模型在不断变化环境中的表现。同时，结合强化学习优化对话策略，增强系统的交互能力。还计划引入多模态信息（如图像、声音），实现更丰富的多源融合，满足复杂应用场景的需求。
AI Executive Summary
In recent years, conversational recommender systems (CRS) have gained prominence due to their ability to deliver personalized suggestions through natural language interactions. However, effectively inferring user preferences from limited conversational context remains a significant challenge, especially when integrating diverse external data sources such as knowledge graphs and user reviews. Traditional fusion methods often struggle with semantic mismatches across heterogeneous data modalities, limiting recommendation accuracy and response quality.
This paper introduces C2-CRS, a novel framework that employs a hierarchical coarse-to-fine contrastive learning approach to address these issues. The core idea is to extract multi-grained semantic units—such as words, entities, and subgraphs—from different data signals and align them progressively. In the coarse stage, the model maximizes global semantic similarity across modalities, effectively capturing overall user preferences. The fine stage refines this alignment at the entity and word level, ensuring detailed semantic consistency. This dual-stage training enables the system to fuse multi-source information more coherently, leading to richer user representations.
Extensive experiments on Redial and DuRecDial datasets demonstrate that C2-CRS significantly outperforms existing models, with an 8.5% increase in recommendation recall and a 6.2% improvement in dialogue BLEU scores. Ablation studies confirm that both coarse and fine contrastive stages contribute substantially to performance gains. The approach also exhibits strong robustness under data scarcity and noisy conditions, highlighting its practical viability.
By bridging the semantic gap across heterogeneous data, this work advances the state-of-the-art in multi-source data fusion for CRS. Its scalability and extensibility make it suitable for real-world applications such as personalized e-commerce and intelligent customer support. Future directions include incorporating dynamic data updates, reinforcement learning for dialogue strategies, and multi-modal data integration, aiming to develop more adaptive, context-aware conversational agents that better understand and serve user needs.
Deep Dive
Limitations & Outlook
What gaps remain?
Abstract
Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.