Working Papers

Algorithmic vs. Friend-based Recommendations in Shaping Novel Content Engagement: A Large-scale Field Experiment
with Shan Huang and Leyu Lin (WeChat)
- Currently under Major Revision at Information Systems Research.
- The early version of this work was presented at China India Insights Program (CIIP) 2024, The Twenty-Fifth ACM Conference of Economics and Computation (EC’24), INFORMS Marketing Science Conference 2025, Conference on Information Systems and Technology (CIST) 2025.
Abstract: This study identifies the differential impact of algorithmic and friend-based recommendations-the two predominant mechanisms of online content recommendation-on users’ engagement with novel information, characterized as diverse and non-redundant. Our analysis focuses on the influence of different content recommended by algorithms versus friends and the role of social influence, specifically the impact of social cues inherent in friend-based recommendations. We designed and conducted a large-scale field experiment on WeChat, involving 2.1 million users. Participants were randomly assigned to one of three groups: a control group that received content recommended by algorithms, a treatment group that viewed content shared by friends with visible social cues (e.g., friends’ “likes”), and another treatment group that was exposed to friend-shared content with the social cues hidden. The findings reveal a general preference for less novel content across all groups. However, the presence of social cues significantly mitigated this trend, indicating that social influence can encourage engagement with more novel information. Despite algorithms tending to recommend content of lower novelty, users engage more with novel content when recommended by algorithms than by friends with and without social cues. The study also discovered significant variations in engagement with novel content among users of different genders, ages, and city tiers. These results carry important implications for the design of content recommendation systems and inform policymaking regarding the dissemination of information online.

“The Strength of Weak Ties” Varies Across Viral Channels
with Shan Huang and Yuan Yuan
Abstract: The diffusion of novel information through social networks is essential for dismantling echo chambers and promoting innovation. Our study examines how two major types of viral channels, specifically Direct Messaging (DM) and Broadcasting (BC), impact the well-known “strength of weak ties” in disseminating novel information across social networks. We conducted a large-scale empirical analysis, examining the sharing behavior of 500,000 users over a two-month period on a major social media platform. Our results suggest a greater capacity for DM to transmit novel information compared to BC, although DM typically involves stronger ties. Furthermore, the “strength of weak ties” is only evident in BC, not in DM where weaker ties do not transmit significantly more novel information. Our mechanism analysis indicates that the content selection by both senders and recipients, contingent on tie strength, contributes to the observed differences between these two channels. These findings expand both our understanding of contemporary weak tie theory and our knowledge of how to disseminate novel information in social networks.

Work in Progress

LLMs in Product Selection for Small E-commerce
with Shan Huang
- The early version of this work was presented at INFORMS Annual Meeting 2025.
Abstract: Product selection is a persistent challenge for small businesses. With limited data and technical expertise, these firms often rely on intuition rather than evidence. Recent advances in large language models (LLMs) offer a promising alternative for supporting data-driven decisions in resource-constrained settings. We propose an LLM-based product selection framework that emphasizes comparative rather than pointwise prediction. We show that LLMs perform poorly without contextual references, but that constructing reference groups and enabling comparative reasoning substantially improves predictive accuracy. However, larger reference groups reduce performance, and different grouping strategies lead to meaningfully different outcomes. Our approach significantly outperforms competitive machine learning models in predicting product-selection outcomes. These findings provide practical guidance for small businesses making product decisions and offer new insights into how LLMs leverage relational information in decision-making tasks.

LLM-based Digital Twins in Market Research
with Shan Huang, Tong Wang, and Jinyong Ma (ByteDance)