计算机科学
推荐系统
偏爱
注意力网络
知识图
图形
钥匙(锁)
骨料(复合)
情报检索
代表(政治)
人工智能
理论计算机科学
复合材料
法学
材料科学
微观经济学
经济
政治
计算机安全
政治学
作者
Qiming Li,Zhao Zhang,Fuzhen Zhuang,Yongjun Xu,Chao Li
出处
期刊:ACM Transactions on Information Systems
日期:2023-04-08
卷期号:41 (4): 1-23
摘要
Recently, recommender systems based on knowledge graphs (KGs) have become a popular research direction. Graph neural network (GNN) is the key technology of KG-based recommendation systems. However, existing GNNs have a significant flaw: They cannot explicitly model users’ intent in recommendations. Intent plays an essential role in users’ behaviors. For example, users may first generate an intent to purchase a certain group of items and then select a specific item from the group based on their preferences. Therefore, explicitly modeling intent has a positive significance for improving recommendation performance and providing explanations for recommendations. In this article, we propose a new model called Topic-aware Intention Network (TIN) for explainable recommendations with KGs. TIN models user representations from both preference and intent views. Specifically, we design a relational attention graph neural network to selectively aggregate information in KG to learn user preferences, and we propose a knowledge-enhanced topic model to learn user intent, which is viewed as topics hidden in user behavior sequences. Finally, we obtain the user representation by fusing user preference and intent through an attention network. The experimental results show that our proposed model outperforms the state-of-the-art methods and can generate reasonable explanations for the recommendation results.
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