About Me:

I am an Assistant Professor of Marketing at McDonough School of Business, Georgetown University. My research employs econometrics, statistics, and machine learning methods to investigate: (1) the designs of digital platforms (e.g., recommendation algorithms and business models); (2) the decision-making processes of content creators and influencers.

My work has been published in leading marketing journals, including the Journal of Marketing Research and Marketing Science. I am honored to have received several awards for my research, including the 2021 Vithala R. and Saroj V. Rao ISMS Doctoral Dissertation Award (winner), the 2020 Shankar-Spiegel Dissertation Proposal Award (runner-up), and the finalist of the 2020 Best Doctoral Dissertation Proposal Competition at the American Statistics Association (Marketing Section). I have also served as an ad hoc reviewer for Marketing Science, Management Science, and the Journal of Marketing Research, etc.

RA Hiring: I hire part-time research assistants with solid quantitative skills in econometrics, computer science, or data science.

For more about my background, check out my CV.

Publications:

• The Daily Me versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform
  Liu, Jia and Ziwei Cong
  Journal of Marketing Research, 2022
  
  • Featured in “My Journal Read” by American Marketing Association
  [ Show Abstract ] [ Paper ]
  

  Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.

Working Papers:

• Understanding Users’ Content Contribution Behavior When Content Can Be Priced
  Cong, Ziwei, Ying Zhao, Zilei Zhang
  Accepted at Marketing Science


• The Role of “Live” in Livestreaming Markets: Evidence using Orthogonal Random Forest.
  Cong Ziwei, Jia Liu and Puneet Manchanda
  Reject and Resubmit at Marketing Science
  
  
• Recommendation Algorithms on User-Generated Content in Social Networks: the Quantity-Quality Dilemma
  Cong Ziwei, Jia Liu
  Manuscript in preparation
  
  
• Understanding the Evolvement of Multi-User Activities on Content Platforms: A Multi-Dynamic Neural Poisson System.
  Yin Mingzhang, Ziwei Cong and Jia Liu
  Manuscript in preparation

Selected Research in Progress:

• Supplier Response to Matchmaking in Business-to-Business E-commerce: Spillover and Heterogeneity
  with Chen Yi, Yixing Chen and Liwen Hou