Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics  

作　　者：Dinesh Kalla Nathan Smith Fnu Samaah Kiran Polimetla Dinesh Kalla;Nathan Smith;Fnu Samaah;Kiran Polimetla(Department of Doctoral Studies, Colorado Technical University, Colorado Springs, CO, USA;Department of Computer Science, Harrisburg University of Science and Technology, Harrisburg, PA, USA;Adobe Technology Service (ATS) Department, Adobe, San Jose, CA, USA)

机构地区：[1]Department of Doctoral Studies, Colorado Technical University, Colorado Springs, CO, USA [2]Department of Computer Science, Harrisburg University of Science and Technology, Harrisburg, PA, USA [3]Adobe Technology Service (ATS) Department, Adobe, San Jose, CA, USA

出　　处：《Journal of Data Analysis and Information Processing》2024年第1期76-88,共13页数据分析和信息处理（英文）

摘　　要：This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].

关 键 词：Azure Data Lake U-SQL Author Recommendation System Power BI Microsoft Academic Big Data Word Embedding 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]