Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation (2504.17427v1)

Abstract: Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a whole, neglecting the inherent complexity and entanglement within the dialogue. Specifically, a dialogue comprises both focus information and background information, which mutually influence each other. Current methods tend to model these two types of information mixedly, leading to misinterpretation of users' actual needs, thereby lowering the accuracy of recommendations. To address this issue, this paper proposes a novel model to introduce contextual disentanglement for improving conversational recommender systems, named DisenCRS. The proposed model DisenCRS employs a dual disentanglement framework, including self-supervised contrastive disentanglement and counterfactual inference disentanglement, to effectively distinguish focus information and background information from the dialogue context under unsupervised conditions. Moreover, we design an adaptive prompt learning module to automatically select the most suitable prompt based on the specific dialogue context, fully leveraging the power of LLMs. Experimental results on two widely used public datasets demonstrate that DisenCRS significantly outperforms existing conversational recommendation models, achieving superior performance on both item recommendation and response generation tasks.

Overview of "Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation"

The paper "Beyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation" presents an innovative approach to enhancing conversational recommender systems (CRS) by introducing a novel model called DisenCRS. This model addresses the challenges of accurately interpreting user needs by disentangling the complex and intertwined focus and background information within dialogue contexts, a limitation in existing dialogue modeling approaches.

Key Contributions

The authors articulate the necessity of separating focus information (related to entities) from background information (context unrelated to entities) in dialogue, arguing that current methods' holistic modeling often misinterpret user intent. To address this, DisenCRS employs a dual disentanglement framework composed of:

1. Self-Supervised Contrastive Disentanglement: This technique distinguishes between focus and background information via contrastive learning. It utilizes entity-related information as proxy signals to guide the disentanglement process, ensuring that focus and background information are represented distinctly in the model's latent space.
2. Counterfactual Inference Disentanglement: This leverages counterfactual reasoning to further refine the disentanglement process. By analyzing the absence of either focus or background information, the model evaluates the influence of each information type on user decision-making, thereby enhancing the disentanglement's effectiveness.

Complementing the disentanglement framework, DisenCRS incorporates an adaptive prompt learning module. This module dynamically selects appropriate prompts from a constructed prompt pool based on the dialogue context. It ensures that both focus and background information are optimally exploited to enhance CRS performance in item recommendation and response generation tasks.

Experimental Findings

Empirical results from experiments conducted using two well-established conversational datasets, ReDial and INSPIRED, demonstrate that DisenCRS outperforms competitive baselines across multiple metrics, including Recall@k, NDCG@k, and MRR@k. The model shows a marked improvement in accurately recommending items by effectively leveraging disentangled contextual information. Additionally, in response generation tasks, DisenCRS excels in producing more informative and fluent dialogues, validated by both automatic evaluations and human assessments.

Implications and Future Directions

The implications of this research are twofold: Practically, DisenCRS offers an approach to significantly improve the performance of CRS by addressing the disentanglement of context, which is crucial for accurately capturing user intent. Theoretically, it introduces innovative methods in disentanglement learning, which can be of interest to researchers exploring dialogue systems and natural language processing.

Looking ahead, this paper's novel approach opens various avenues for future studies. Researchers could investigate LLMs' potential in further refining disentanglement processes. Additionally, optimizing the adaptive prompt learning module with more sophisticated mechanisms may enhance its ability to dynamically tailor responses based on nuanced user interactions.

In conclusion, by moving beyond whole dialogue modeling, DisenCRS marks a significant step in the evolution of conversational recommender systems, offering an enriched framework for understanding and responding to complex user dialogue interactions.