Simulations in information access (IA) have recently gained interest, as shown by various tutorials and workshops around that topic. Simulations can be key contributors to central IA research and evaluation questions, especially around interactive settings when real users are unavailable, or their participation is impossible due to ethical reasons. In addition, simulations in IA can help contribute to a better understanding of users, reduce complexity of evaluation experiments, and improve reproducibility. Building on recent developments in methods and toolkits, the second iteration of our Sim4IA workshop aims to again bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.

Simulations in various forms have been used to evaluate information access systems, like search engines, recommender systems, or conversational agents. In the form of the Cranfield paradigm, a simulation setup is well-known in the IR community, but user simulations have recently gained interest. While user simulations help to reduce the complexity of evaluation experiments and help with reproducibility, they can also contribute to a better understanding of users. Building on recent developments in methods and toolkits, the Sim4IA workshop aims to bring together researchers and practitioners to form an interactive and engaging forum for discussions on the future perspectives of the field. An additional aim is to plan an upcoming TREC/CLEF campaign.

Scientific publishing heavily relies on the assessment of quality of submitted manuscripts by peer reviewers. Assigning a set of matching reviewers to a submission is a highly complex task which can be performed only by domain experts. We introduce RevASIDE, a reviewer recommendation system that assigns suitable sets of complementing reviewers from a predefined candidate pool without requiring manually defined reviewer profiles. Here, suitability includes not only reviewers’ expertise, but also their authority in the target domain, their diversity in their areas of expertise and experience, and their interest in the topics of the manuscript. We present three new data sets for the expert search and reviewer set assignment tasks and compare the usefulness of simple text similarity methods to document embeddings for expert search. Furthermore, an quantitative evaluation demonstrates significantly better results in reviewer set assignment compared to baselines. A qualitative evaluation also shows their superior perceived quality.