Abstract:
In this paper, we propose a novel graph convolutional network (GCN)-based recommendation method using both knowledge graph (KG) and review texts to solve the cold start p...Show MoreMetadata
Abstract:
In this paper, we propose a novel graph convolutional network (GCN)-based recommendation method using both knowledge graph (KG) and review texts to solve the cold start problem and provide explainability. In our model, GCN-based collaborative filtering (CF) is parallelly performed on the user-item interaction graph and the KG to utilize the items’ additional information in the recommendation process. Also, we use a pretrained language model to generate embeddings from the user reviews and utilize them in the GCN embedding propagation process to reflect the users’ subjective sentiment and opinion. After the recommendation is performed, we generate the paths from the target user to recommended items by using the KG and embeddings from review texts. To prove the superiority of the proposed method, we conduct the experiment by comparing recommendation performance with baseline models. In the experiment, the proposed method outperformed the other models.
Published in: 2023 IEEE International Conference on Big Data (BigData)
Date of Conference: 15-18 December 2023
Date Added to IEEE Xplore: 22 January 2024
ISBN Information: