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

arXiv: https://arxiv.org/abs/2204.10254 · HTML: https://mlsanity.com/paper/2204.10254/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2022.

## Built on by (most cited first, 9 of 9)

- [Cocoa: Co-Planning and Co-Execution with AI Agents](https://mlsanity.com/paper/2412.10999/) (2024)
- [PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers](https://mlsanity.com/paper/2403.02939/) (2024)
- [How Far Are AI Scientists from Changing the World?](https://mlsanity.com/paper/2507.23276/) (2025)
- [Personalized Jargon Identification for Enhanced Interdisciplinary Communication](https://mlsanity.com/paper/2311.09481/) (2023)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [Augmenting Scientific Creativity with Retrieval across Knowledge Domains](https://mlsanity.com/paper/2206.01328/) (2022)
- [How Data Scientists Review the Scholarly Literature](https://mlsanity.com/paper/2301.03774/) (2023)

## Abstract (arXiv)

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.