Workshop Details

ExUM workshop aims to provide a forum for discussing problems, challenges, and innovative research approaches in this area by investigating the role of transparency and explainability in recent methodologies for building user models or developing personalized and adaptive systems.

Adaptive and personalized systems have become pervasive technologies, gradually playing an increasingly important role in our daily lives. Indeed, we are now used to interacting with algorithms that help us in several scenarios, ranging from services that suggest music or movies to personal assistants who proactively support us in complex decision-making tasks. As the importance of such technologies in our everyday lives grows, it is fundamental that the internal mechanisms that guide these algorithms are as clear as possible. It is not by chance that the EU General Data Protection Regulation (GDPR) emphasized the users' right to explanation when people face intelligent systems. Unfortunately, current research tends to go in the opposite direction since most of the approaches try to maximize the effectiveness of the personalization strategy (e.g., recommendation accuracy) at the expense of model explainability.

Topics of interests include but are not limited to:

Transparent and Explainable Personalization Strategies

o   Scrutable User Models

o   Transparent User Profiling and Personal Data Extraction

o   Explainable Personalization and Adaptation Methodologies

o   Novel strategies (e.g., conversational recommender systems) for building transparent algorithms

o   Transparent Personalization and Adaptation to Groups of users

Designing Explanation Algorithms

o   Explanation algorithms based on item description and item properties

o   Explanation algorithms based on user-generated content (e.g., reviews)

o   Explanation algorithms based on collaborative information

o   Building explanation algorithms for opaque personalization techniques (e.g., neural networks, matrix factorization)

o   Explanation algorithms based on methods to build group models

Designing Transparent and Explainable User Interfaces

o   Transparent User Interfaces

o   Designing Transparent Interaction methodologies

o   Novel paradigms (e.g. chatbots) for building transparent models

Evaluating Transparency and Explainability

o   Evaluating Transparency in interaction or personalization

o   Evaluating Explainability of the algorithms

o   Designing User Studies for evaluating transparency and explainability

o   Novel metrics and experimental protocols

Open Issues in Transparent and Explainable User Models and Personalized Systems

o   Ethical issues (Fairness and Biases) in User Models and Personalized Systems

o   Privacy management of Personal and Social data

o   Discussing Recent Regulations (GDPR) and future directions