Abstract
Knowledge Graph (KG) is of great help in improving the performance of recommendation systems. Graph neural networks (GNNs) based model has gradually become the mainstream of knowledge-aware recommendation (KGR). However, existing GNN-based KGR models underutilize the semantic information in KG to enhance collaborative features. Therefore, we propose a Collaborative Knowledge Graph-Aware framework (CKGA). In general, we first use the knowledge graph to obtain the semantic representation of items and users, and then feed these representations into the Collaborative Filtering (CF) model to obtain better collaborative features. Specifically, (1) we design a novel CF model to learn the collaborative features of items and users, which partitions the interaction graph into different subgraphs of similar interest and performs high-order graph convolution inside subgraphs. (2) For learning important semantic information in KG, we design an attribute aggregation scheme and an inference mechanism for GNN which directly propagates further attributes and inference information to the central node. Extensive experiments conducted on three public datasets demonstrate the superior performance of CKGA over the state-of-the-arts.
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This work was supported in part by National Key Research and Development Program of China (2022YFF0904301).
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Zhu, L., Zhang, Y., Li, G. (2024). Enhancing Collaborative Features with Knowledge Graph for Recommendation. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14333. Springer, Singapore. https://doi.org/10.1007/978-981-97-2387-4_13
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