Insights from the long-tail: Learning latent representations of online user behavior in the presence of skew and sparsity

Adit Krishnan, Ashish Sharma, Hari Sundaram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes an approach to learn robust behavior representations in online platforms by addressing the challenges of user behavior skew and sparse participation. Latent behavior models are important in a wide variety of applications: recommender systems; prediction; user profiling; community characterization. Our framework is the first to jointly address skew and sparsity across graphical behavior models. We propose a generalizable bayesian approach to partition users in the presence of skew while simultaneously learning latent behavior profiles over these partitions to address user-level sparsity. Our behavior profiles incorporate the temporal activity and links between participants, although the proposed framework is flexible to introduce other definitions of participant behavior. Our approach explicitly discounts frequent behaviors and learns variable size partitions capturing diverse behavior trends. The partitioning approach is data-driven with no rigid assumptions, adapting to varying degrees of skew and sparsity. A qualitative analysis indicates our ability to discover niche and informative user groups on large online platforms. Results on User Characterization (+6-22% AUC); Content Recommendation (+6-43% AUC) and Future Activity Prediction (+12-25% RMSE) indicate significant gains over state-of-the-art baselines. Furthermore, user cluster quality is validated with magnified gains in the characterization of users with sparse activity.

Original languageEnglish (US)
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Pages297-306
Number of pages10
ISBN (Electronic)9781450360142
DOIs
StatePublished - Oct 17 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: Oct 22 2018Oct 26 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other27th ACM International Conference on Information and Knowledge Management, CIKM 2018
Country/TerritoryItaly
CityTorino
Period10/22/1810/26/18

Keywords

  • Behavior Analysis
  • Behavior Skew
  • Data Sparsity
  • Interactive Media Platforms
  • Probabilistic Graphical Models

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting

Fingerprint

Dive into the research topics of 'Insights from the long-tail: Learning latent representations of online user behavior in the presence of skew and sparsity'. Together they form a unique fingerprint.

Cite this