Abstract
Knowledge graph-based recommendation methods are a hot research topic in the field of recommender systems in recent years. As a mainstream knowledge graph-based recommendation method, the propagation-based recommendation method captures users’ potential interests in items by integrating the representations of entities and relations in the knowledge graph and the high-order connection patterns between entities to provide personalized recommendations. For example, the collaborative knowledge-aware attentive network (CKAN) is a typical state-of-the-art propagation-based recommendation method that combines user-item interactions and knowledge associations in the knowledge graph, and performs heterogeneous propagation in the knowledge graph to generate multi-hop ripple sets, thereby capturing users’ potential interests. However, existing propagation-based recommendation methods, including CKAN, usually ignore the complex relations between entities in the multi-hop ripple sets and do not distinguish the importance of different ripple sets, resulting in inaccurate user potential interests being captured. Therefore, this paper proposes a top-N recommendation method named collaborative knowledge-aware graph attention network (CKGAT). Based on the heterogeneous propagation strategy, CKGAT uses the knowledge-aware graph attention network to extract the topological proximity structures of entities in the multi-hop ripple sets and then learn high-order entity representations, thereby generating refined ripple set embeddings. CKGAT further uses an attention aggregator to perform weighted aggregation on the ripple set embeddings, the user/item initial entity set embeddings, and the original representations of items to generate accurate user embeddings and item embeddings for the top-N recommendations. Experimental results show that CKGAT, overall, outperforms three baseline methods and six state-of-the-art propagation-based recommendation methods in terms of recommendation accuracy, and outperforms four representative propagation-based recommendation methods in terms of recommendation diversity.
Highlights
Introduction iations recommendation technology has achieved considerable development and has been widely used in various domains, recommender systems still suffer from several challenges, such as inaccurate recommendations, data sparsity, and cold-start problems.In recent years, introducing a knowledge graph (KG) into the recommender system as side information has attracted considerable research interest, and the knowledge graphbased recommendation method has recently become a hot research topic in the field of recommender systems [1].Knowledge graphs have proven to be effective in improving recommendation performance and alleviating the aforementioned challenges, because the knowledge graph can provide background knowledge for users and items in the recommender system, which helps to more accurately capture user preferences for items
We propose a novel method called collaborative knowledge-aware graph attention network (CKGAT) for top-N recommendation
CKGAT was compared with nine representative methods which are divided into four categories: collaborative filtering method (BPRMF), embedding-based recommendation method (CKE), connection-based recommendation method (KPRN), and propagation-based recommendation methods which are further divided into the user representation-refinement approaches (RippleNet and collaborative knowledge-aware attentive network (CKAN)), the item representationrefinement approaches (KGCN and KGNN-LS), and both user and item representationrefinements approaches (KGAT and knowledge graph-based intent network (KGIN))
Summary
Introduction iations recommendation technology has achieved considerable development and has been widely used in various domains, recommender systems still suffer from several challenges, such as inaccurate recommendations, data sparsity, and cold-start problems.In recent years, introducing a knowledge graph (KG) into the recommender system as side information has attracted considerable research interest, and the knowledge graphbased recommendation method has recently become a hot research topic in the field of recommender systems [1].Knowledge graphs have proven to be effective in improving recommendation performance and alleviating the aforementioned challenges, because the knowledge graph can provide background knowledge for users and items in the recommender system, which helps to more accurately capture user preferences for items. The core idea of the embedding-based recommendation methods, such as collaborative knowledge-base embedding (CKE) [2], is leveraging the fruitful facts in the knowledge graph to enrich the representations of users and item. The core idea of connection-based recommendation methods, such as knowledge-aware path recurrent network (KPRN) [3], is using the connection patterns between entities (users/items) in the knowledge graph to guide recommendation. The core idea of the propagation-based recommendation methods, such as collaborative knowledge-aware attentive network (CKAN) [4], knowledge graph convolutional networks (KGCN) [5], and knowledge graph-based intent network (KGIN) [6], is integrating entity representations, relation representations, and highorder connection patterns between entities to provide more personalized recommendation
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