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Using an explicit query and a topic model for scientific article recommendation.
Boussaadi Smail1, Hassina Aliane2, Ouahabi Abdeldjalil3
1DTISI, Research Center on Scientific and Technical Information Cerist, Algiers, Algeria.
Education and Information Technologies
|June 26, 2023
Summary
Researchers can find relevant scientific articles more easily with a new content-based filtering method. This approach uses semantic exploration and topic modeling to improve research productivity by delivering objective and relevant results.
Keywords:
Latent Dirichlet AllocationNon-negative Matrix FactorizationScientific articleScientific recommendationTopic modelingMore Related Videos
Area of Science:
- Information Science
- Computer Science
- Bibliometrics
Background:
- The increasing volume of scientific literature presents challenges for researchers seeking relevant articles.
- Traditional search methods in digital databases can be time-consuming and hinder productivity.
- A need exists for advanced methods to efficiently identify pertinent research across domains.
Purpose of the Study:
- To propose a novel method for recommending scientific articles using content-based filtering.
- To address the challenge of identifying relevant research irrespective of the researcher's domain.
- To develop an optimal topic model for enhancing the scientific article recommendation process.
Main Methods:
- Utilizing content-based filtering techniques for article recommendation.
- Employing semantic exploration with latent factors to understand article content.
- Developing and optimizing a topic model to underpin the recommendation system.
Main Results:
- The proposed method demonstrates effective scientific article recommendation.
- The system provides relevant and objective results, meeting researcher needs.
- Performance expectations were confirmed through empirical experiences.
Conclusions:
- The novel recommendation method enhances the discovery of relevant scientific articles.
- Semantic exploration and topic modeling are effective for cross-domain research.
- This approach has the potential to significantly improve researcher productivity.