A Recommendation Algorithm Combining Local and Global Interest Features
-
Published:2023-04-14
Issue:8
Volume:12
Page:1857
-
ISSN:2079-9292
-
Container-title:Electronics
-
language:en
-
Short-container-title:Electronics
Author:
Song Xiaoyuan12, Qin Jiwei12, Ren Qiulin12
Affiliation:
1. School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China 2. Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi 830046, China
Abstract
Due to the ability of knowledge graph to effectively solve the sparsity problem of collaborative filtering, knowledge graph (KG) has been widely studied and applied as auxiliary information in the field of recommendation systems. However, existing KG-based recommendation methods mainly focus on learning its representation from the neighborhood of target items, ignoring the influence of other items on the target item. The learning focuses on the local feature representation of the target item, which is not sufficient to effectively explore the user’s preference degree for the target item. To address the above issues, in this paper, an approach combining users’ local interest features with global interest features (KGG) is proposed to efficiently explore the user’s preference level for the target item, which learns the user’s local interest features and global interest features for target item through Knowledge Graph Convolutional Network and Generative Adversarial Network (GAN). Specifically, this paper first utilizes the Knowledge Graph Convolutional Network to mine related attributes on the knowledge graph to effectively capture item correlations and obtain the local feature representation of the target item, then uses the matrix factorization method to learn the user’s local interest features for target items. Secondly, it uses GAN to learn the user’s global interest features for target items from the implicit interaction matrix. Finally, a linear fusion layer is designed to effectively fuse the user’s local and global interests towards target items to obtain the final click prediction. Experimental results on three real datasets show that the proposed method not only effectively integrates the user’s local and global interests but also further alleviates the problem of data sparsity. Compared with the current baselines for knowledge graph-based systems, the KGG method achieves a maximum improvement of 8.1% and 7.6% in AUC and ACC, respectively.
Funder
the Science Fund for Outstanding Youth of Xinjiang Uygur Autonomous Region
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference45 articles.
1. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions;Adomavicius;IEEE Trans. Knowl. Data Eng.,2005 2. Zhang, Z.-P., Kudo, Y., Murai, T., and Ren, Y.-G. (2019). Enhancing Recommendation Accuracy of Item-Based Collaborative Filtering via Item-Variance Weighting. Appl. Sci., 9. 3. Wang, P., Yang, J., and Zhang, J. (2018). A Strategy toward Collaborative Filter Recommended Location Service for Privacy Protection. Sensors, 18. 4. Zhu, J., Li, K., Peng, J., and Qi, J. (2023). Self-Supervised Graph Attention Collaborative Filtering for Recommendation. Electronics, 12. 5. Dietz, L., Kotov, A., and Meij, E. (2018, January 8–12). Utilizing Knowledge Graphs for Text-Centric Information Retrieval. Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, MI, USA.
|
|