Track chairs:

  • Nadia Fawaz (Pinterest, USA)
  • Mounia Lalmas (Spotify, UK)
  • Ji-Rong Wen (Renmin University, China)


We invite research contributions to the User Modeling, Personalization and Inclusion track at the 30th edition of the Web Conference series (formerly known as WWW), to be held April 19-23, 2021 in Ljubljana, Slovenia (

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 enhance user experience 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, to system design. In this track we invite original research submissions addressing all aspects of user modeling and experience, personalization, as well as inclusive modeling and system design.

Topics include (but are not limited to):

User modeling and Experience

  • User Modeling, Log Analysis, and Interaction Models
  • User Interfaces, Design and Evaluation
  • User Experience with Interactive and Conversational Systems
  • Innovative Methods to Enhance Online User Experience
  • Qualitative Methods to Collect and Analyze User Feedback
  • Practical Large-scale Studies of User Experience
  • Experiment Design for User Studies and Online Testing


  • Personalized Content Ranking and Presentation
  • User Privacy Protection in Personalized Search
  • Machine learning for Personalized Search and Recommender Systems
  • Personalization and Recommendation 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
  • Fairness-aware retrieval and ranking
  • Diversity for Search and Recommendation Systems
  • Explainable Methods for Personalization

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

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