We introduce a conversational recommender that unifies collaborative filtering signals with a large language model through a Context-Aware Dynamic Embedding Fusion module. A graph-based collaborative filtering backbone learns latent user and item representations, and lightweight adapters inject these embeddings into the language model during fine-tuning. The fusion module uses a learned gate that conditions on dialogue context, model confidence, and user interaction depth to control the contribution of collaborative filtering and language representations at both representation and scoring stages. This adaptive design steers the model toward language knowledge under cold start and toward collaborative filtering when sufficient interactions are available, which yields robust personalization across user regimes. Training follows a multiobjective scheme that promotes confidence-aligned and history-aware gating while preserving the generative and ranking abilities of the language model. We evaluate using Recall@K, NDCG@K and MRR@K on widely used conversational recommendation datasets and compare against strong collaborative filtering and language baselines. Results show consistent gains with the largest improvements in cold start settings, and ablation and gate analyses indicate meaningful turn-level variation conditioned on dialogue context, uncertainty, and user warmth.
Yoon et al. (Thu,) studied this question.
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