支持冷启动用户推荐的区块链服务发布方案

董学文; 刘昊哲; 乔慧; 郑佳伟

doi:10.11959/j.issn.1000-436x.2021032

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支持冷启动用户推荐的区块链服务发布方案

学术论文 | 更新时间：2024-06-05

- 支持冷启动用户推荐的区块链服务发布方案
- Blockchain-based service publishing scheme with recommendation for cold start users
- 通信学报 2021年42卷第1期页码：57-66
- 作者机构：
  
  西安电子科技大学计算机科学与技术学院，陕西西安 710071
- 作者简介：
  
  [ "董学文（1981- ），男，湖北黄冈人，博士，西安电子科技大学教授，主要研究方向为区块链、认知无线电网络、无线网络安全和隐私。" ]
  [ "刘昊哲（1996- ），男，陕西咸阳人，西安电子科技大学硕士生，主要研究方向为区块链、无线网络安全和隐私。" ]
  [ "乔慧（1992- ），女，甘肃陇南人，西安电子科技大学博士生，主要研究方向为区块链隐私、安全协议设计与分析。" ]
  [ "郑佳伟（1995- ），男，山西原平人，西安电子科技大学硕士生，主要研究方向为区块链、物联网安全。" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB1400700);国家重点研发计划基金资助项目(2020YFB1005500);国家自然科学基金资助项目(61972310);国家自然科学基金资助项目(61972017);国家自然科学基金资助项目(62072487)
- DOI：10.11959/j.issn.1000-436x.2021032
  中图分类号： TP302
- 网络出版日期：2021-01，
  
  纸质出版日期：2021-01-25
- 稿件说明：
移动端阅览
董学文, 刘昊哲, 乔慧, 等. 支持冷启动用户推荐的区块链服务发布方案[J]. 通信学报, 2021,42(1):57-66.

Xuewen DONG, Haozhe LIU, Hui QIAO, et al. Blockchain-based service publishing scheme with recommendation for cold start users[J]. Journal on communications, 2021, 42(1): 57-66.
董学文, 刘昊哲, 乔慧, 等. 支持冷启动用户推荐的区块链服务发布方案[J]. 通信学报, 2021,42(1):57-66. DOI： 10.11959/j.issn.1000-436x.2021032.

Xuewen DONG, Haozhe LIU, Hui QIAO, et al. Blockchain-based service publishing scheme with recommendation for cold start users[J]. Journal on communications, 2021, 42(1): 57-66. DOI： 10.11959/j.issn.1000-436x.2021032.

摘要

为解决现有的发布系统容易受到篡改，以及很难向新加入系统的“冷启动”用户推荐服务信息的问题，提出了一种支持冷启动用户推荐的区块链服务发布方案。将单个辅助域的潜在特征映射模型扩展到2个辅助域，为目标域中的冷启动用户进行更为精确的推荐。将微服务架构和区块技术相结合，确保系统可扩展性、可靠性和安全性。在亚马逊交易数据中提取的3个真实数据集上的实验结果表明，所提推荐模型优于大多数其他推荐方法，并且服务发布信息可以安全地存储在区块链中以确保其不被更改。

Abstract

In order to solve the problem that the existing publishing systems were vulnerable to tampering

and it was difficult to recommend service information to “cold start” users

a blockchain-based service publishing scheme that supported cold-start user recommendations was proposed.The latent feature mapping model of a single auxiliary domain was extended to two auxiliary domains to make more accurate recommendations for cold start users in the target domain.In addition

the microservice architecture and block technology were combined to ensure system scalability

reliability and security.The experimental results of three real data sets extracted from Amazon transaction data show that the proposed recommendation model is better than most other recommendation methods

and the service release information can be safely stored in the blockchain to ensure that it is not changed.

关键词

Keywords

references

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