Cold-Start Resilient Recommendation via Dynamical Heterogeneous Graph Embedding

This research explores a crucial problem in recommendation systems: cold-start scenarios. The paper likely proposes a novel approach using dynamical heterogeneous graph embedding to improve recommendation accuracy when limited user-item interaction data is available.

• •Addresses the cold-start problem in recommender systems.
• •Utilizes dynamical heterogeneous graph embedding techniques.
• •Aims to improve recommendation accuracy with limited data.