Toine Bogers’s research while affiliated with University of Copenhagen and other places

Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters could have a big impact on job seekers, companies and society as a whole. In this paper, we analyze the search formulation and source selection behavior of the recruiters at one of Scandinavia’s largest job portals and recruitment agencies using search logs for close to 18,000 recruitment search tasks. We provide an analysis of the search sessions of recruiters in terms search tactics, query operators, query length, term re-use and filter usage, and break down their behavior both by task type and task complexity. We also relate their short-term tactics to different learning stages in the search process and investigate their influence on search success. We find that identifying and assessing relevant candidates for a job posting is a complex task: recruiters usually submit multiple queries during sessions that can last for hours and that increase in complexity. Recruiters all spend more time per query as their session progresses. We also observed query reformulation strategies that indicate distinct patterns of knowledge gaining during sessions. Relating these tactics to positive responses from candidates we aim at predicting successful strategies.

Multistakeholder recommender systems are those that account for the impacts and preferences of multiple groups of individuals, not just the end users receiving recommendations. Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this article, we focus our discussion on the intricacies of the evaluation of multistakeholder recommender systems. We bring attention to the different aspects involved in the evaluation of multistakeholder recommender systems - from the range of stakeholders involved (including but not limited to producers and consumers) to the values and specific goals of each relevant stakeholder. Additionally, we discuss how to move from theoretical principles to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. We aim to provide guidance to researchers and practitioners about how to think about these complex and domain-dependent issues of evaluation in the course of designing, developing, and researching applications with multistakeholder aspects.

Multistakeholder recommender systems are defined as those that account for ``the preferences of multiple parties when generating recommendations, especially when these parties are on different sides of the recommendation interaction.'' Due to their complexity, evaluating these systems cannot be restricted to the overall utility of a single stakeholder, as is often the case of more mainstream recommender system applications. In this chapter, we focus our discussion on the intricacies involved in understanding what is the ``right'' construct required to ensure the proper evaluation of multistakeholder recommender systems. We bring attention to the different aspects involved in the evaluation of multistakeholder recommender systems---from the range of stakeholders involved (beyond producers and consumers) to the values and specific goals of each relevant stakeholder. Additionally, we discuss how to move from theoretical evaluation to practical implementation, providing specific use case examples. Finally, we outline open research directions for the RecSys community to explore. Our aim in this section is to provide guidance to researchers and practitioners about how to think about these complex and domain-dependent issues in the course of designing, developing, and researching applications with multistakeholder aspects. This chapter is part of the following Dagstuhl report: https://www.dagstuhl.de/de/seminars/seminar-calendar/seminar-details/24211

Purpose In this paper, we introduce the C RISPS (CRoss-domaIn relevance aSPects Scheme) coding scheme for complex information needs in the four leisure domains of books, games, movies and music. It categorizes the relevance aspects people consider when searching for these resources. The coding scheme and findings help search engines to better support complex information needs, both by prioritizing which aspects are easier to classify automatically and by determining which information sources should be considered. Design/methodology/approach A cross-domain classification scheme for relevance aspects and information needs in casual leisure domains (C RISPS ) is developed and applied. The paper provides the documentation of the scheme development and annotation process as well as a detailed, large-scale analysis of 2000 requests (500 per domain) and relevance aspects for four domains as expressed in complex search requests in everyday life information seeking posted to online forums. Findings We identify and discuss relevance aspect frequencies, information need types and the described search process of the requests. Furthermore, the coding scheme development and the annotation process are documented and reflected on. Originality/value This is the first categorization and analysis of complex information needs in these four leisure domains combined. The coding scheme and findings can be used to develop new types of search interfaces that incorporate the kinds of relevance aspects identified in the scheme, allowing to express complex needs in the form of structured queries.

While there have been several studies on how users experience algorithmic recommendations and their explanations, we know relatively little about human recommendations and which item aspects humans highlight when describing their own recommendation needs. A better understanding of human recommendation behavior could help us design better recommender systems that are more attuned to their users. In this paper, we take a step towards such understanding by analyzing a Reddit community dedicated to requesting and providing for recommendations: $\textsf {/r/}\textsf {ifyoulikeblank}$. After a general analysis of the community, we provide a more detailed analysis of the prevalent music requests and the example items used to ask for these recommendations. Finally, we compare these human recommendations to algorithmic recommendations to better characterize their differences. We conclude by discussing the implications of our work for recommender systems design.