TITLE:
Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics
AUTHORS:
Dinesh Kalla, Nathan Smith, Fnu Samaah, Kiran Polimetla
KEYWORDS:
Azure Data Lake, U-SQL, Author Recommendation System, Power BI, Microsoft Academic, Big Data, Word Embedding
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.12 No.1,
February
8,
2024
ABSTRACT: 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].