Implementing Recommendation Algorithms in a Large-Scale Biomedical Science Knowledge Base

TL;DR

This paper discusses the implementation and evaluation of various recommendation algorithms within the Meta platform, a large-scale biomedical knowledge base with over 27 million articles, to enhance literature discovery and researcher support.

Contribution

It introduces scalable recommendation algorithms tailored for a large biomedical knowledge network, leveraging diverse datasets like citation, text, and co-authorship information.

Findings

Algorithms effectively utilize citation and semantic data.

Scalability to large datasets demonstrated.

Challenges in deploying real-time recommendations identified.

Abstract

The number of biomedical research articles published has doubled in the past 20 years. Search engine based systems naturally center around searching, but researchers may not have a clear goal in mind, or the goal may be expressed in a query that a literature search engine cannot easily answer, such as identifying the most prominent authors in a given field of research. The discovery process can be improved by providing researchers with recommendations for relevant papers or for researchers who are dealing with related bodies of work. In this paper we describe several recommendation algorithms that were implemented in the Meta platform. The Meta platform contains over 27 million articles and continues to grow daily. It provides an online map of science that organizes, in real time, all published biomedical research. The ultimate goal is to make it quicker and easier for researchers to: filter through scientific papers; find the most important work and, keep up with emerging research results. Meta generates and maintains a semantic knowledge network consisting of these core entities: authors, papers, journals, institutions, and concepts. We implemented several recommendation algorithms and evaluated their efficiency in this large-scale biomedical knowledge base. We selected recommendation algorithms that could take advantage of the unique environment of the Meta platform such as those that make use of diverse datasets such as a citation networks, text content, semantic tag content, and co-authorship information and those that can scale to very large datasets. In this paper, we describe the recommendation algorithms that were implemented and report on their relative efficiency and the challenges associated with developing and deploying a production recommendation engine system.