计算机科学
嵌入
推荐系统
特征学习
情报检索
背景(考古学)
文字嵌入
代表(政治)
任务(项目管理)
矩阵分解
光学(聚焦)
语义相似性
相似性(几何)
人工智能
理论计算机科学
机器学习
图像(数学)
古生物学
特征向量
物理
管理
光学
量子力学
政治
政治学
法学
经济
生物
作者
Phu Pham,Loan T. T. Nguyen,Ngoc Thanh Nguyen,Witold Pedrycz,Unil Yun,Jerry Chun‐Wei Lin,Bay Vo
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-09-01
卷期号:53 (9): 6027-6040
被引量:2
标识
DOI:10.1109/tcyb.2022.3233819
摘要
Recent studies on heterogeneous information network (HIN) embedding-based recommendations have encountered challenges. These challenges are related to the data heterogeneity of the associated unstructured attribute or content (e.g., text-based summary/description) of users and items in the context of HIN. In order to address these challenges, in this article, we propose a novel approach of semantic-aware HIN embedding-based recommendation, called SemHE4Rec. In our proposed SemHE4Rec model, we define two embedding techniques for efficiently learning the representations of both users and items in the context of HIN. These rich-structural user and item representations are then used to facilitate the matrix factorization (MF) process. The first embedding technique is a traditional co-occurrence representation learning (CoRL) approach which aims to learn the co-occurrence of structural features of users and items. These structural features are represented for their interconnections in terms of meta-paths. In order to do that, we adopt the well-known meta-path-based random walk strategy and heterogeneous Skip-gram architecture. The second embedding approach is a semantic-aware representation learning (SRL) method. The SRL embedding technique is designed to focus on capturing the unstructured semantic relations between users and item content for the recommendation task. Finally, all the learned representations of users and items are then jointly combined and optimized while integrating with the extended MF for the recommendation task. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed SemHE4Rec in comparison with the recent state-of-the-art HIN embedding-based recommendation techniques, and reveal that the joint text-based and co-occurrence-based representation learning can help to improve the recommendation performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI