Using an explicit query and a topic model for scientific article recommendation

作     者：Smail, Boussaadi Aliane, Hassina Abdeldjalil, Ouahabi 

作者机构：DTISI Research Center on Scientific and Technical Information Cerist Algiers Algeria Director of Information Sciences R&D Laboratory Head of Natural Language Processing and Digital Content Team Cerist Algiers Algeria 1.Polytech Tours Imaging anBrain University of Tours INSERM U930 Tours France 

出 版 物：《Education and Information Technologies》 (Educ. Inf. Technol.)

年 卷 期：2023年第28卷第12期

页      面：15657-15670页

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 1205[管理学-图书情报与档案管理] 0401[教育学-教育学] 04[教育学] 

主　　题：Latent Dirichlet Allocation Non-negative Matrix Factorization Scientific article Scientific recommendation Topic modeling 

摘      要：The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher s productivity. This article proposes a new method of recommending scientific articles that takes advantage of content-based filtering. The challenge is to target relevant information that meets a researcher s needs, regardless of their research domain. Our recommendation method is based on semantic exploration using latent factors. Our goal is to achieve an optimal topic model that will serve as the basis for the recommendation process. Our experiences confirm our performance expectations, showing relevance and objectivity in the results. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.