Article
Geography, Physical
Shengwen Li, Renyao Chen, Chenpeng Sun, Hong Yao, Xuyang Cheng, Zhuoru Li, Tailong Li, Xiaojun Kang
Summary: This study proposes a region-aware neural graph collaborative filtering (RA-NGCF) model that improves the accuracy of personalized recommendations by introducing geographical regions. The experiment results show that introducing region entities can enhance the effectiveness of personalized recommendations.
INTERNATIONAL JOURNAL OF DIGITAL EARTH
(2022)
Article
Mathematics
Zhiqiang Pan, Honghui Chen
Summary: This research introduces the Collaborative Knowledge-Enhanced Recommendation (CKER) method, which utilizes a collaborative graph convolution network (CGCN) to learn user and item representations and incorporates self-supervised learning to maximize mutual information between user preferences. Experimental results demonstrate that CKER outperforms state-of-the-art baselines in the field of knowledge-enhanced recommendation.
Article
Computer Science, Artificial Intelligence
Bei Hui, Lizong Zhang, Xue Zhou, Xiao Wen, Yuhui Nian
Summary: The paper presents a recommendation system that utilizes auxiliary information from knowledge graphs and mines user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. The proposed method, ReBKC, shows significant improvement compared to state-of-the-art methods on three datasets, verifying the effectiveness of learning short-term and long-term user preferences and integrating knowledge graphs to deeply identify user preferences.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yi-Ching Chou, Chiao-Ting Chen, Szu-Hao Huang
Summary: This study proposes a graphical deep collaborative filtering (GraphDCF) algorithm for providing personalized mutual fund recommendations. By constructing a graph-structured network to model different latent relationships among customers with similar shopping habits, and utilizing a deep embedded collaborative filtering framework to predict customers' willingness to purchase mutual funds, the algorithm outperforms other frequently used methods according to experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Chemistry, Analytical
Meng Jian, Chenlin Zhang, Xin Fu, Lifang Wu, Zhangquan Wang
Summary: Recommender systems help users filter items of interest from massive multimedia content. This study proposes a knowledge-aware multispace embedding learning (KMEL) method for personalized recommendation, which leverages the semantic correlations between items to better model users' interests. Experimental results on real-world datasets demonstrate the effectiveness of the proposed KMEL model.
Article
Computer Science, Artificial Intelligence
Yi Zhu, Xindong Wu, Jipeng Qiang, Yunhao Yuan, Yun Li
Summary: The CAPR method uses collaborative autoencoder for personalized recommendation, learning feature representations of users and items to address different characteristics and sparsity issues. Experimental results demonstrate the effectiveness of this method compared to others.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Xueping Su, Jiao He, Jie Ren, Jinye Peng
Summary: This paper proposes a personalized Chinese tourism recommendation algorithm based on the Knowledge Graph, and significant improvements are achieved in the experiments.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Shenghao Liu, Bang Wang, Xianjun Deng, Laurence T. Yang
Summary: This paper proposes a novel recommendation algorithm that combines self-attentive graph convolution network, latent group mining, and collaborative filtering. Experimental results show that the algorithm outperforms the state-of-the-art algorithms on real-world datasets.
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Yishuai Geng, Yi Zhu, Yun Li, Xiaobing Sun, Bin Li
Summary: Personalized recommendation systems have been developed to address information overload and help users make quick decisions. Autoencoder-based models are commonly used in these systems due to their effective representation learning and lack of labeled data requirements. However, the scarcity of auxiliary information and the neglect of hidden relations between features significantly affect recommendation accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Business
Xiao Sha, Zhu Sun, Jie Zhang
Summary: The paper introduces a new hierarchical attentive knowledge graph embedding framework for effective recommendation. It extracts expressive subgraphs and encodes them to generate effective subgraph embeddings for enhanced user preference prediction. Extensive experiments show the superiority of the framework against state-of-the-art recommendation methods.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Jia Xuefeng, Li Cunbin, Zhou Ying
Summary: This study uses knowledge graphs and collaborative filtering techniques to recommend experts for projects in the electric power field. The research results show that knowledge graphs can effectively solve the cold-start problem in collaborative filtering, and the proposed model can improve the relevance of the recommendation results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Yuefang Gao, Zhen-Wei Huang, Zi-Yuan Huang, Ling Huang, Yingjie Kuang, Xiaojun Yang
Summary: Recently, neighborhood-based collaborative filtering has been used more and more in personalized recommender systems. However, the traditional approach of selecting a fixed number of nearest users/items as neighbors has limitations. To address this issue, a new recommender system called Multi-scale Broad Collaborative Filtering (MBCF) is proposed, which captures rich information from different numbers of nearest users/items. Instead of using deep neural networks (DNNs), the Broad Learning System (BLS) is adopted to learn the complex nonlinear relationships between users and items, achieving satisfactory recommendation performance while avoiding overfitting. Extensive experiments on eight benchmark datasets demonstrate the effectiveness of the proposed MBCF algorithm.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Kaiyang Ma, Zhenyu Yang, Yu Wang, Laiping Cui, Wenfeng Jiang
Summary: This paper proposes a novel sequential recommendation method that fuses collaborative transformations and temporally aware target interaction networks to automatically learn dynamic changes in user interests, improving recommendation performance.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Kang Liu, Feng Xue, Dan Guo, Le Wu, Shujie Li, Richang Hong
Summary: This paper addresses the mismatch problem between multimodal feature extraction and user interest modeling and proposes a novel model called MEGCF, which better models multimodal user preferences.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhibin Hu, Jiachun Wang, Yan Yan, Peilin Zhao, Jian Chen, Jin Huang
Summary: The neural graph personalized ranking (NGPR) model proposed in this study directly incorporates the user-item interaction graph in embedding learning to address the lack of correlation between users and items in existing methods. Extensive experiments demonstrate the superior performance of NGPR on personalized ranking tasks.
KNOWLEDGE-BASED SYSTEMS
(2021)
