Ivana Williams

A Method for Automated Feed Generation Based on User’s Research Interests

Meta is a tool that helps scientists discover biomedical research. We organize and track over 67 million researchers, diseases, genes, proteins, pathways, and more — including full coverage of papers from PubMed and preprints from bioRxiv. In our platform currently, a feed comprises of a set of papers which are retrieved from our knowledge graph using a boolean query, composed from different entities from our knowledge graph (e.g. UMLS concepts, MeSH terms, journals, authors, etc.) and ranked based on their relevancy, publication date, and impact. In this paper, we describe a method used to automate and personalize a new users' onboarding experience, by building an automated feed (autofeed) based on minimal input from the user. Users' inputs, for example, may include keywords, free text, papers that are related to their research interests, etc. The goal of our method is to, given the user's inputs, compose an expanded query that retrieves papers that are highly specific and related to the user's research interests. This method, in turn, enables us to create a highly personalized and flexible user experience within our platform. We describe the following components of the autofeeds algorithm: data ingestion and pre-processing, document embedding which leverages the state of the art contextual embedding models, such as BioSentVec and BioBERT, hierarchical document clustering and the query composition approach. We conclude with a discussion of our qualitative and quantitative evaluation of independent components and the full onboarding process.

Ivana Williams is a Staff Research Scientist at the Chan Zuckerberg Initiative, Meta team working on unlocking scientific insights from scientific publications. She is applying the state of the art machine learning and natural language processing methods to the generation of knowledge graphs and recommendation systems, research interest modeling, extraction and disambiguation of scientific elements and relationships as well as the development of novel predictive capabilities. Most recently she has been leading personalization efforts within Meta, including personalized user onboarding via personalized feed generation and recommendations. She holds a Master's degree in Mathematics and Statistics from Georgetown University.

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