SPORENLP: A Spatial Recommender System for Scientific Literature
Abstract
SPORENLP is a recommendation system designed to review scientific literature. It operates on a sub-dataset comprising 15,359 publications, with a total of 117,941,761 pairwise comparisons. This dataset includes both metadata comparisons and text-based similarity aspects obtained using natural language processing (NLP) techniques.Unlike other recommendation systems, SPORENLP does not rely on specific aspect features. Instead, it identifies the top k candidates based on shared keywords and embedding-related similarities between publications, enabling content-based, intuitive, and adjustable recommendations without excluding possible candidates through classification. To provide users with an intuitive interface for interacting with the dataset, we developed a web-based front-end that takes advantage of the principles of spatial hypertext. A qualitative expert evaluation was conducted on the dataset. The dataset creation pipeline and the source code for SPORENLP will be made freely available to the research community, allowing further exploration and improvement of the system.
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| Titel | SPORENLP: A Spatial Recommender System for Scientific Literature |
|---|---|
| Medien | Proceedings of the 19th International Conference on Web Information Systems and Technology (WEBIST'23) |
| Verlag | SciTePress |
| Heft | --- |
| Band | 2023 |
| ISBN | 978-989-758-672-9 |
| Verfasser/Herausgeber | Johannes Wirth, Daniel Roßner, Prof. Dr. René Peinl, Prof. Dr. Claus Atzenbeck |
| Seiten | 429–436 |
| Veröffentlichungsdatum | 15.11.2023 |
| Projekttitel | --- |
| Zitation | Wirth, Johannes; Roßner, Daniel; Peinl, René; Atzenbeck, Claus (2023): SPORENLP: A Spatial Recommender System for Scientific Literature. Proceedings of the 19th International Conference on Web Information Systems and Technology (WEBIST'23) 2023, S. 429–436. DOI: 10.5220/0012210400003584 |