TITLE:
Recommender System for Information Retrieval Using Natural Language Querying Interface Based in Bibliographic Research for Naïve Users
AUTHORS:
Mohamed Chakraoui, Abderrafiaa Elkalay, Naoual Mouhni
KEYWORDS:
Recommender Systems, Collaborative Filtering, Apriori Algorithm, Natural Language Understanding, Bibliographic Research
JOURNAL NAME:
International Journal of Intelligence Science,
Vol.12 No.1,
January
27,
2022
ABSTRACT: With the increasing of data on the internet, data analysis has become
inescapable to gain time and efficiency, especially in bibliographic
information retrieval systems. We can estimate the number of actual scientific
journals points to around 40,000 with about four million articles published each year. Machine
learning and deep learning applied to recommender systems had become
unavoidable whether in industry or in research. In this current, we propose an
optimized interface for bibliographic information retrieval as a running example, which allows different kind of
researchers to find their needs following some relevant criteria through
natural language understanding. Papers indexed in Web of Science and Scopus are
in high demand. Natural language including text and linguistic-based
techniques, such as tokenization, named entity recognition, syntactic and
semantic analysis, are used to express natural language queries. Our Interface
uses association rules to find more related papers for recommendation. Spanning
trees are challenged to optimize the search process of the system.