Tag Archives: matching

Learning Faces to Predict Matching Probability in an Online Dating Market

Kwon, Soonjae, Sung-Hyuk Park, Gene Moo Lee, Dongwon Lee (2021) “Learning Faces to Predict Matching Probability in an Online Dating Market”. Work-in-progress.

  • Under review for a conference presentation.
  • Based on an industry collaboration

With the increasing use of online matching markets, predicting the matching probability among users is crucial for better market design. Although previous studies have constructed visual features to predict the matching probability, facial features extracted by deep learning have not been widely used. By predicting user attractiveness in an online dating market, we find that deep learning-enabled facial features can significantly enhance prediction accuracy. We also predict the attractiveness at various evaluator groups and explain their different preferences based on the theory of evolutionary psychology. Furthermore, we propose a novel method to visually interpret deep learning-enabled facial features using the latest deep learning-based generative model. Our work contributes to IS researchers utilizing facial features using deep learning and interpreting them to investigate underlying mechanisms in online matching markets. From a practical perspective, matching platforms can predict matching probability more accurately for better market design and recommender systems for maximizing the matching outcome.

Targeting Pre-Roll Ads using Video Analytics

Park, Sungho, Gene Moo Lee, Donghyuk Shin, Sang-Pil Han. “Targeting Pre-Roll Ads using Video Analytics”, Under Reject ana Resubmit, Management Science. [Submitted: April 25, 2021]

  • Funded by Sauder Exploratory Research Grant 2020
  • Presented at Southern Methodist University (2020), University of Washington (2020), INFORMS (2020), WITS (2020), HKUST (2021), Maryland (2021), American University (2021)
  • Research assistants: Raymond Situ, Miguel Valarao

Pre-roll video ads continue to rise at an unparalleled pace, creating new opportunities and challenges. They are more immersive than conventional banner ads and must be viewed at least partially before the content video is played. On the other hand, the prevailing skippable format of pre-roll video ads that allows viewers to skip ads after five seconds generates opportunity costs for advertisers and online platforms when the ad is skipped. Against this backdrop, we propose a novel video analytics method for improving pre-roll video ad performance by extracting multi-modal (audio, video, text) properties from both video ads and content videos using deep learning and signal processing techniques, and then analyzing their effect on video ad completion. The findings indicate that the ad-content congruence in various modalities is essential in explaining viewers’ ad completion. Specifically, visual congruence (i.e., celebrity overlap in ad and content) and textual congruence (i.e., topic similarity of ad and content) play important roles as viewers may shape ex-ante expectations of the congruence based on visual cues (i.e., thumbnail images) and previous experience (i.e., watched content clips from the same program) before watching the content video. We also discover, through predictive analyses, that video ad completion can be reliably predicted by features derived from the proposed method. Surprisingly, there is no discernible loss of predictive power when analyzing only the first five seconds of ads and content videos rather than their entire length, resulting in significant cost savings when processing large video datasets.

Matching Mobile Applications for Cross Promotion (ISR 2020)

Lee, Gene Moo, Shu He, Joowon Lee, Andrew B. Whinston (2020) Matching Mobile Applications for Cross-Promotion. Information Systems Research 31(3), pp. 865-891.

  • Based on an industry collaboration with IGAWorks
  • Presented in Chicago Marketing Analytics (Chicago, IL 2013), WeB (Auckland, New Zealand 2014), Notre Dame (2015), Temple (2015), UC Irvine (2015), Indiana (2015), UT Dallas (2015), Minnesota (2015), UT Arlington (2015), Michigan State (2016), Korea Univ (2021)
  • Dissertation Paper #3
  • Research assistant: Raymond Situ

The mobile applications (apps) market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find the apps that fit their needs. Cross-promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app-promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and postdownload usages) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using latent Dirichlet allocation topic modeling on apps’ production descriptions and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine-learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross-promotion campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. This paper has implications on the trade-off between utility and privacy in the growing mobile economy.

Link Formation in Mobile and Economic Networks: Model and Empirical Analysis (Ph.D. Dissertation 2015)

Gene Moo Lee (2015). Link Formation in Mobile and Economic Networks: Model and Empirical AnalysisUT Austin Ph.D. Dissertation, Austin, TX, August 2015.

In this dissertation, we study three link formation problems in mobile and economic networks: (i) company matching for mergers and acquisitions (M&A) network in the high-technology (high-tech) industry, (ii) mobile application (app) matching for cross-promotion network in mobile app markets, and (iii) online friendship formation in mobile social networks. Each problem can be modeled as link formation problem in a graph, where nodes represent independent entities (e.g., companies, apps, users) and edges represent interactions (e.g., transactions, promotions, friendships) among the nodes.

First, we propose a new data-analytic approach to measure firms’ dyadic business proximity to analyze M&A network in the high-tech industry. Specifically, our method analyzes the unstructured texts that describe firms’ businesses using latent Dirichlet allocation (LDA) topic modeling, and constructs a novel business proximity measure based on the output. Using CrunchBase data including 24,382 high-tech companies and 1,689 M&A transactions, we empirically validate our business proximity measure in the context of industry intelligence and show the measure’s effectiveness in an application of M&A network analysis. Based on the research, we build a cloud-based information system to facilitate competitive intelligence on the high-tech industry.

Second, we analyze mobile app matching for cross promotion network in mobile app markets. Cross promotion (CP) is a new app promotion framework, in which a mobile app is promoted to the users of another app. Using IGAWorks data covering 1,011 CP campaigns, 325 apps, and 301,183 users, we evaluate the effectiveness of CP campaigns in comparison with existing ad channels such as mobile display ads. While CP campaigns, on average, are still suboptimal as compared with display ads, we find evidence that a careful matching of mobile apps can significantly improve the effectiveness of CP campaigns. Our empirical results show that app similarity, measured by LDA from apps’ text descriptions, is a significant factor that increases the user engagement in CP campaigns. With this observation, we propose an app matching mechanism for the CP network to improve the ad effectiveness.

Third, we study friendship network formation in a location-based social network. We build a structural model of social link creation that incorporates individual characteristics and pairwise user similarities. Specifically, we define four user proximity measures from biography, geography, mobility, and short messages (i.e., tweets). To construct proximity from unstructured text information, we build LDA topic models of user biography texts and tweets. Using Gowalla data with 385,306 users, three million locations, and 35 million check-in records, we empirically estimate the structural model to find evidence on the homophily effect in network formation.