# A Scalable Hybrid Research Paper Recommender System for Microsoft Academic

## TL;DR

This paper describes a large-scale hybrid research paper recommender system for Microsoft Academic that combines citation and content data to improve recommendation quality for millions of papers and patents.

## Contribution

The paper introduces a scalable hybrid recommendation approach that handles incomplete data and balances novelty and authority, evaluated through user studies and available for further research.

## Key findings

- Strong correlation between user scores and system rankings
- Content-based recommendations need improved precision
- System effectively balances citation and content approaches

## Abstract

We present the design and methodology for the large scale hybrid paper recommender system used by Microsoft Academic. The system provides recommendations for approximately 160 million English research papers and patents. Our approach handles incomplete citation information while also alleviating the cold-start problem that often affects other recommender systems. We use the Microsoft Academic Graph (MAG), titles, and available abstracts of research papers to build a recommendation list for all documents, thereby combining co-citation and content based approaches. Tuning system parameters also allows for blending and prioritization of each approach which, in turn, allows us to balance paper novelty versus authority in recommendation results. We evaluate the generated recommendations via a user study of 40 participants, with over 2400 recommendation pairs graded and discuss the quality of the results using P@10 and nDCG scores. We see that there is a strong correlation between participant scores and the similarity rankings produced by our system but that additional focus needs to be put towards improving recommender precision, particularly for content based recommendations. The results of the user survey and associated analysis scripts are made available via GitHub and the recommendations produced by our system are available as part of the MAG on Azure to facilitate further research and light up novel research paper recommendation applications.