Call for papers

Most of the current information access systems, such as search, retrieval and recommendation systems, are data-driven and heavily rely on advanced machine learning algorithms to model and predict user preferences, needs or intents. While the latest generation of such systems excels in producing highly accurate lists of retrieved contents, they often lack interpretability and are based on black-box models. These systems fail to explicitly integrate fundamental psychological mechanisms that shape and influence user preferences, behaviors, intents, and decision-making as a whole. In stark contrast, the field of psychology has a long-standing tradition of rigorous scientific research to formulate comprehensive theories and models aimed at explaining the human mind and behavior. Much of this research entails controlled experiments, empirical studies, and data analysis, all designed to gain deeper insights into various facets of human cognition, emotions, behavior, and mental processes.

The PsyIAS workshop bridges the disciplines of machine learning and psychology, aiming to connect the research communities of information retrieval, recommender systems, and natural language processing with cognitive and behavioral psychology. It serves as a forum for exchanging ideas and engaging in multidisciplinary discussions about the use of psychological constructs, theories, and empirical findings for modeling and predicting user preferences, intents, and behaviors. In particular, research about incorporating such psychology-inspired models into the search, retrieval, and recommendation processes, and corresponding algorithms and systems, are in the focus of PsyIAS. On a more fundamental level, we are also interested in research that looks into the role of cognitive processes underlying human information access.

Concrete topics of interest include, but are not limited to: cognition-inspired, personality-aware, and affect-aware algorithmic content ranking systems, which leverage, for instance, models of human memory, attention, affect, personality, and aesthetic preferences; in addition, research that sheds light on the relationship between human and algorithmic decision-making, as well as decision biases that shape the users' interactions with a retrieval or recommendation system, is highly welcome; so is work that discusses how insights from psychology for adopting a user-centric perspective on the design and evaluation of information access systems.

We solicit full research papers (min. 10, max. 12 pages, no appendix) and short position papers (min. 4, max. 6 pages, no appendix). Submissions must be original, not submitted, under review, or accepted to any other outlet. All submissions should be prepared according to the CEUR-WS formatting instructions . If you use Overleaf, you can directly access the template here.

Each submission will be reviewed based on quality, originality, clarity, and relevance to the workshop. The reviewing process will be double-blind. Therefore, authors must conceal their identity (no author names, no affiliations, no acknowledgment of sponsors, no direct references to own previous work like “In [X], we showed...”). Authors should also refrain from uploading the submitted work to any open-access repository, such as arXiv, before a decision is rendered. While we do not strictly prohibit submissions of papers that are already available on arXiv, we strongly urge authors to take measures that make it harder to find their submission on arXiv (at least change the paper title), in that case.

Authors of accepted papers will be invited to present their work as part of the workshop and at least one author of each accepted paper must attend the workshop to present the work. We will seek opportunities to publish extended versions of selected papers through a journal special issue. Please use EasyChair to submit your manuscripts. 


Important dates:


If you have any questions, do not hesitate contact us at psyias2024@jku.at