From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks

TL;DR

This paper proposes methods to enhance scientific paper recommendations by integrating implicit social network information, such as author and citation connections, to improve user engagement and discovery experience.

Contribution

It introduces new techniques for augmenting recommendations with textual relevance messages based on knowledge-graph connections, including author and citation associations, and evaluates their effectiveness.

Findings

Significantly increased user engagement with recommendations.

Effective expansion of message coverage for users with limited history.

No bias introduced towards highly-cited authors.

Abstract

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for \em augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement -- and future engagement when mediated by authors -- without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.