In this thesis, we research interactive recommender systems and present a method to offer interactive recommendations in the form of recommender settings. Specifically, this is done in the domain of job recommendations at XING, a professional social network. These settings allow users to tune some aspects of the job recommender system, i.e. their preferred career level, whether they are willing to commute or even move to a new location, and which topics (skills, jobroles and disciplines) they like or dislike. These topics are explicitly not taken from the users' profiles, as profiles on XING rather reflect the CV of the user, i.e. things that the user did in the past but not what the user aims to work on in the future. Instead, we generate the topics from the job recommendations we already offer, which are influenced by the users' profiles, their behavior on the platform as well as from their previously specified recommender settings. These topics can thus be seen as a summary of the users' job recommendations. By tweaking the recommendation settings, the actual job recommendations immediately change which in turn has an influence on the selectable topics thus allowing the user to interactively refine the recommendation settings and explore the item space. We implemented our recommender settings approach in the back-end of the actual job recommendation service, thus turning XING's job recommender into an interactive recommender service. Moreover, we implemented a prototype application that allows users to experience the interactive job recommendations. Given both the adjusted job recommender service and our prototype, we conducted both a large-scale quantitative evaluation as well as a user study in which we collected qualitative feedback and analyzed the impact on user satisfaction.

@mastersthesis{IRSFAPSNK17,
	author	 = {Mirko Köster},
	title	 = {{Interactive Recommender Systems For A Professional Social Network}},
	advisors	 = {Julian Kunkel},
	year	 = {2017},
	month	 = {06},
	school	 = {Universität Hamburg},
	howpublished	 = {{Online \url{https://wr.informatik.uni-hamburg.de/_media/research:theses:mirko_koester_interactive_recommender_systems_for_a_professional_social_network.pdf}}},
	type	 = {Master's Thesis},
	abstract	 = {In this thesis, we research interactive recommender systems and present a method to offer interactive recommendations in the form of recommender settings. Specifically, this is done in the domain of job recommendations at XING, a professional social network. These settings allow users to tune some aspects of the job recommender system, i.e. their preferred career level, whether they are willing to commute or even move to a new location, and which topics (skills, jobroles and disciplines) they like or dislike. These topics are explicitly not taken from the users' profiles, as profiles on XING rather reflect the CV of the user, i.e. things that the user did in the past but not what the user aims to work on in the future. Instead, we generate the topics from the job recommendations we already offer, which are influenced by the users' profiles, their behavior on the platform as well as from their previously specified recommender settings. These topics can thus be seen as a summary of the users' job recommendations. By tweaking the recommendation settings, the actual job recommendations immediately change which in turn has an influence on the selectable topics thus allowing the user to interactively refine the recommendation settings and explore the item space. We implemented our recommender settings approach in the back-end of the actual job recommendation service, thus turning XING's job recommender into an interactive recommender service. Moreover, we implemented a prototype application that allows users to experience the interactive job recommendations. Given both the adjusted job recommender service and our prototype, we conducted both a large-scale quantitative evaluation as well as a user study in which we collected qualitative feedback and analyzed the impact on user satisfaction.},
}