通信学报 ›› 2021, Vol. 42 ›› Issue (1): 57-66.doi: 10.11959/j.issn.1000-436x.2021032

• 学术论文 • 上一篇    下一篇

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   

  1. 西安电子科技大学计算机科学与技术学院, 陕西 西安 710071
  • 修回日期:2020-12-01 出版日期:2021-01-25 发布日期:2021-01-01
  • 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。
  • 基金资助:
    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);陕西省重点研发计划基金资助项目(2019ZDLGY12-03);陕西省重点研发计划基金资助项目(2019ZDLGY13-06)

Blockchain-based service publishing scheme with recommendation for cold start users

Xuewen DONG, Haozhe LIU, Hui QIAO, Jiawei ZHENG   

  1. School of Computer and Technology, Xidian University, Xi’an 710071, China
  • Revised:2020-12-01 Online:2021-01-25 Published:2021-01-01
  • Supported by:
    The National Key Research and Development Program of China(2017YFB1400700);The National Key Research and Development Program of China(2020YFB1005500);The National Natural Science Foundation of China(61972310);The National Natural Science Foundation of China(61972017);The National Natural Science Foundation of China(62072487)

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。

关键词: 区块链, 服务发布, 冷启动推荐, 辅助域

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.

Key words: blockchain, service publishing, cold start recommendation, auxiliary domain

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