A Recommendation Method for Electronic Components Based on Knowledge Graph
Authors: 
Xudong Yu, Yanhui Zhou, Fei Pu, Guilian ZhangAuthors Info & Claims
Xudong Yu, Yanhui Zhou, Fei Pu, Guilian ZhangAuthors Info & ClaimsPages 451 - 455
Abstract
Enterprises are facing significant challenges due to electronic component shortages and the need for domestic alternatives. In this paper, we propose the integration of knowledge graphs and recommender system technologies to address these issues. First, the recommended model in this paper adopts the idea of alternating learning to treat recommendation task and knowledge graph embedding task as two relatively independent modules, and designs an information fusion unit to fuse user behavior information in recommendation task with entity structure information in knowledge graph embedding task. Second, the Word2Vec word vector model was used to extract electronic component characteristic word vectors as part of the initialization of electronic component entity nodes in the knowledge graph embedding task, introducing semantic features to the model. Finally, the proposed method achieves an AUC of 91.5% and an ACC of 85.8% in electronic component recommendation. The experimental results indicate that the method is feasible.
References
[1]
Zhang Fuzheng, Yuan N J, Lian Defu, Collaborative knowledge base embedding for recommender systems [C]//Proc of the22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York: ACM Press, 2016:353-362.
[2]
Zhao Huan, Yao Quanming, Li Jianda, Meta-graph based recommendation fusion over heterogeneous information networks [C]77Proc of the 23rd ACM SICKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2017: 635-644.
[3]
Padhi A K, Mohanty A, Sahoo S. FindMoviez:a movie recommendation system[ M]//Intelligent Systems. Berlin: Springer, 2021: 49-57.
[4]
Sun, Zhu, "Research commentary on recommendations with side information: A survey and research directions." Electronic Commerce Research and Applications 37, 2019: 100879.
[5]
Javed, Umair, A review of content-based and context-based recommendation systems. International Journal of Emerging Technologies in Learning (iJET) 16.3, 2021: 274-306.
[6]
Natarajan S, Vairavasundaram S, Natarajan S, Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data[J].Expert Systems with Applications, 2020, 149: 113248.
[7]
Paparrizos I, Cambazoglu B B, Gionis A. Machine learned job recommendation[C]//Proc of the 5th ACM Conference on Recommender Systems. New York: ACM Press, 2011: 325-328.
[8]
Singh A, Rose C, Visweswariah K, PROSPECT: a system for screening candidates for recruitment[C]//Proc of the 19th ACM International Conference on Information and Knowledge Management. New York: ACM Press, 2010: 659-668.
[9]
Gao J, Xin X, Liu J, Fine-grained deep knowledge-aware network for news recommendation with self-attention[C]. 2018EEE/WICIACM International Conference on Web Intelligence (WI). IEEE, 2018:81-88.
[10]
Niaraki A S, Kim K. Ontology based personalized route planning system using a multi-criteria decision making approach[J]. Expert Systems with Applications, 2009, 36(2p1):2250-2259.
[11]
Oramas S, Ostuni V C, Noia T D, Sound and music recommendation with knowledge graphs[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2016, 8(2): 1-21.
[12]
Grad-Gyenge L, Kiss A, Filzmoser P. Graph embedding based recommendation techniques on the knowledge graph[C]. Adjunct publication of the 25th conference on user modeling, adaptation and personalization. 2017:354-359.
[13]
Wang H, Zhang F,Zhao M, Multi-task feature leaning for knowledge graph enhanced recommendation[C].The world wide web conference. 2019: 2000-2010.
[14]
Shi C, Han X, Song L, Deep collaborative filtering with multi-aspect information in heterogeneous networks[J]. IEEE transactions on knowledge and data engineering, 2019, 33(4):1413-1425.
Index Terms
- A Recommendation Method for Electronic Components Based on Knowledge Graph
Recommendations
A movie recommendation method based on knowledge graph and time series
Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a ...
Graph Embedding Based Recommendation Techniques on the Knowledge Graph
UMAP '17: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and PersonalizationThis paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two ...
Comments
Information & Contributors
Information
Published In
March 2024
676 pages
ISBN:9798400718212
DOI:10.1145/3672919
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 July 2024
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
CSAIDE 2024
CSAIDE 2024: 2024 3rd International Conference on Cyber Security, Artificial Intelligence and Digital Economy
March 1 - 3, 2024
Nanjing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 29Total Downloads
- Downloads (Last 12 months)29
- Downloads (Last 6 weeks)9
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format