User Modeling and Personalization Track
Call for Papers

Track chairs: (

  • Nadia Fawaz (Pinterest, USA)
  • Gabriella Kazai (Microsoft, UK)
  • Rui Li (Pinterest, USA)

We invite research contributions to the User Modeling and Personalization track at the 31st edition of the Web Conference series (formerly known as WWW International World Wide Web Conference), to be held online April 25-29, 2022, hosted by Lyon, France (

A large swath of user interaction with various online services happens through devices connected to the World Wide Web. Hence, developing technologies to understand and personalize user experience in applications powered by web data has become one of the most challenging problems for Web researchers and practitioners. Recent advances have enabled the processing of vast amounts of data collected from these devices, using techniques from machine learning, statistical modeling, natural language processing, speech recognition, computer vision and others. The sheer volume of interactions has also made it possible to innovate and experiment continuously in a data-driven fashion, based on users’ interactions and feedback. Such technology can benefit everyone if it is developed with inclusion at the heart of technical choices, in particular with techniques to mitigate biases that may arise at different stages, from data collection, to modeling and evaluation, to system design. In this track we invite original research submissions addressing all aspects of user modeling, personalization, as well as inclusive modeling and system design, and accessibility.

Topics include (but are not limited to):

User Modeling

  • User Model Development, Log Analysis and Offline Evaluation
  • Qualitative Methods to Collect and Analyze User Feedback
  • User Modeling for Engagement and Interaction Models
  • User Modeling for Content Discovery
  • User Modeling for Interactive and Conversational Systems
  • Innovative Methods to Enhance Online Personalization
  • Practical Large-scale Studies of User Experience
  • Experiment Design for Online Evaluation


  • Personalized Content Ranking and Presentation
  • Machine Learning for Personalized Search and Recommendations
  • Personalization for the Social Web
  • Personalization of the Web Experience
  • Intelligent Agents and Personal Assistants
  • Metrics for User Behavior and Evaluating Success

Inclusive Modeling and System Design

  • Algorithmic Bias, Fairness and Transparency in Personalized Discovery
  • Fairness-aware Retrieval and Ranking
  • Diversity for Search and Recommendation Systems
  • Multi-Sided Fairness in Search and Recommendation
  • Diversity and Fairness Metrics and Evaluation in Personalized Discovery
  • Explainable Methods for Personalization
  • User Privacy Protection in Personalized Systems
  • Accessibility

Submission guidelines, relevant dates, and important policies can be found at