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View article: WITHDRAWN
WITHDRAWN Open
As the internet, smartphones, and review platforms transform how travelers discover urban attractions, understanding this process is critical for harnessing a sustainable urban attraction space. Yet urban discovery remains poorly understoo…
View article: What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems
What Is Serendipity? An Interview Study to Conceptualize Experienced Serendipity in Recommender Systems Open
Serendipity has been associated with numerous benefits in the context of recommender systems, e.g., increased user satisfaction and consumption of long-tail items. Despite this, serendipity in the context of recommender systems has thus fa…
View article: Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto
Dagstuhl Perspectives Workshop 24352 -- Conversational Agents: A Framework for Evaluation (CAFE): Manifesto Open
During the workshop, we deeply discussed what CONversational Information ACcess (CONIAC) is and its unique features, proposing a world model abstracting it, and defined the Conversational Agents Framework for Evaluation (CAFE) for the eval…
View article: NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems
NORMalize 2024: The Second Workshop on Normative Design and Evaluation of Recommender Systems Open
Recommender systems are among the most widely used applications of artificial intelligence. Their use can have far-reaching consequences for users, stakeholders, and society at large. In this second edition of the NORMalize workshop, we on…
View article: NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access Systems
NORMalize: A Tutorial on the Normative Design and Evaluation of Information Access Systems Open
Information access systems, such as Google News or YouTube, increasingly employ algorithms to rank diverse content such as music, recipes, and news articles. Acknowledging the influential role of these algorithms as gatekeepers to online c…
View article: NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems
NORMalize: The First Workshop on Normative Design and Evaluation of Recommender Systems Open
Recommender systems are among the most widely used applications of artificial intelligence. Since they are so widely used, it is important that we, as practitioners and researchers, think about the impact these systems may have on users, s…
View article: Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education
Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education Open
This report documents the program and the outcomes of Dagstuhl Seminar 23031 "Frontiers of Information Access Experimentation for Research and Education", which brought together 38 participants from 12 countries. The seminar addressed tech…
View article: Reality Check – Conducting Real World Studies
Reality Check – Conducting Real World Studies Open
Information retrieval and recommender systems are deployed in real world environments. Therefore, to get a real feeling for the system, we should study their characteristics in “real world studies”. This raises the question: What does it m…
View article: Interpretable Model for Collaborative Filtering Using an Extended Latent Dirichlet Allocation Approach
Interpretable Model for Collaborative Filtering Using an Extended Latent Dirichlet Allocation Approach Open
With the increasing use of AI and ML-based systems, interpretability is becoming an increasingly important issue to ensure user trust and safety. This also applies to the area of recommender systems, where methods based on matrix factoriza…