解决对话推荐系统中数据稀疏问题，提升对冷门物品的推荐效果。

Conversational recommender systems (CRSs) infer user preferences from dialogue contexts, but they suffer from severe data sparsity in both dialogue and entity spaces. Dialogue data are linguistically diverse and open-ended, making it difficult to generalize across varied expressions. Meanwhile, existing CRS models often rely on large knowledge graphs, where only a small fraction of entities receive effective supervision during training, leaving the majority under-trained or entirely unseen at inference time. To address these challenges, we propose DACRS, a novel CRS framework consisting of three modules: Dialogue Augmentation, Knowledge-Guided Entity Modeling, and Dialogue-Entity Matching. The Dialogue Augmentation module adopts a two-stage augmentation pipeline to enrich dialogue contexts and improve robustness to linguistic variation. The Knowledge-Guided Entity Modeling module leverages knowledge graphs through entity substitution and an entity similarity constraint to enhance representation learning for sparsely supervised and unseen entities. Finally, the Dialogue-Entity Matching module integrates dialogue representations with mentioned entity embeddings via dialogue-guided attention aggregation, yielding user representations that capture both explicit and implicit preferences. Extensive experiments on two public benchmark datasets demonstrate that DACRS consistently outperforms state-of-the-art conversational recommender systems.