CKAN: Collaborative Knowledge-aware Attentive Network for Recommender Systems

By Ze Wang, Guangyan Lin, Huobin Tan, Qinghong Chen, Xiyang Liu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

320 citations recorded by the source corpus.

Since it can effectively address the problem of sparsity and cold start of collaborative filtering, knowledge graph (KG) is widely studied and employed as side information in the field of recommender systems. However, most of existing KG-based recommendation methods mainly focus on how to effectively encode the knowledge associations in KG, without highlighting the crucial collaborative signals which are latent in user-item interactions. As such, the learned embeddings underutilize the two kinds of pivotal information and are insufficient to effectively represent the latent semantics of users and items in vector space. In this paper, we propose a novel method named Collaborative Knowledge-aware Attentive Network (CKAN) which explicitly encodes the collaborative signals by collaboration propagation and proposes a natural way of combining collaborative signals with knowledge associations together. Specifically, CKAN employs a heterogeneous propagation strategy to explicitly encode both kinds of information, and applies a knowledge-aware attention mechanism to discriminate the contribution of different knowledge-based neighbors. Compared with other KG-based methods, CKAN provides a brand-new idea of combining collaborative information with knowledge information together. We apply the proposed model on four real-world datasets, and the empirical results demonstrate that CKAN significantly outperforms several compelling state-of-the-art baselines.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/95fae17a5845085204197c1579828207d09e82c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Le Wu
6. Kun Zhang
7. Fei Sun
8. Bin Cui

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Meng Wang
5. Le Wu
6. Kun Zhang
7. Fei Sun
8. Zhenguang Liu

Citations in the corpus

• Learning Intents behind Interactions with Knowledge Graph for Recommendation (2021, methodology): “Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-ofthe-art methods like KGAT [41], KGNN-LS [38], and CKAN [47].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “For instance, KGAT [153], MKGAT [133] and CKAN [161] combine the user-item bipartite graph and knowledge graph into one unified graph by taking the user nodes as one type of entity and the relation between users and items as “interaction".”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, methodology): “More recent studies, such as KGAT [170], CKAN [199],”
• Knowledge Graph Contrastive Learning for Recommendation (2022, unknown): “• CKAN [42].”