Motivating and Supporting User Interaction with Recommender Systems Andreas w. neumann Institute of Information Systems and Management Universitat Karlsruhe(TH), 76128 Karlsruhe, Germany a neumann@iism. uni-karlsruhe, de http://www.iism.uni-karlsruhe.de/a.neumann Abstract. This contribution reports on the introduction of explicit rec- mender systems at the University Library of Karlsruhe. In March 2006, a rating service and a review service were added to the already ex- isting behavior-based recommender system. Logged-in users can write re- ews and rate all library documents(books, journals, multimedia, etc. reading reviews and inspecting ratings are open to the general public A role system is implemented that supports the submission of different eviews for the same document from one user to different user groups ( students, scientists, etc. ) Mechanism design problems like bias and fre riding are discussed, to address these problems the introduction of in centive systems is described. Usage statistics are given and the question which recommender system supports which user needs best, is covered Summing up, recommender systems are a way to combine the support of library user interaction with information access beyond catalog searches Keywords: Recommender system, rating service, review service, mech- anism design, incentive system. 1 Introduction The general public is lately becoming accustomed with recommender systems of different kinds at various online stores. But scientific libraries, where the profit contribution of a product (library document) is not the first concern and the costumers(library users) are coming due to very different incentives, are defini- tively a not less promising application area. Due to the supply complexity or the evaluation of the quality, scientists and students are more and more inca- pable of efficiently finding relevant literature in conventional database oriented catalog systems and search engines. A common solution to this problem lies in asking peers(see e. g. [10). Recommender systems aggregate knowledge from many peer groups to the level of expert advice services. They bear the poten ial to significantly reduce transaction costs for literature searches by means of their aggregation capabilities. Scientific libraries are in a good strategic posi- tion to become(even more than now) the information centers of the future 7 Turning library online public access catalogs(OPAC) into customer oriented service portals supporting the interaction of the customers is one step to this L Kovacs. N. Fuhr, and C. Meghini(Eds ) ECDL 2007. LNCS 4675. pp. 428-139, 2007 ringer-Verlag Berlin Heidelberg 200Motivating and Supporting User Interaction with Recommender Systems Andreas W. Neumann Institute of Information Systems and Management, Universit¨at Karlsruhe (TH), 76128 Karlsruhe, Germany a.neumann@iism.uni-karlsruhe.de http://www.iism.uni-karlsruhe.de/a.neumann Abstract. This contribution reports on the introduction of explicit rec￾ommender systems at the University Library of Karlsruhe. In March 2006, a rating service and a review service were added to the already ex￾isting behavior-based recommender system. Logged-in users can write re￾views and rate all library documents (books, journals, multimedia, etc.); reading reviews and inspecting ratings are open to the general public. A role system is implemented that supports the submission of different reviews for the same document from one user to different user groups (students, scientists, etc.). Mechanism design problems like bias and free riding are discussed, to address these problems the introduction of in￾centive systems is described. Usage statistics are given and the question, which recommender system supports which user needs best, is covered. Summing up, recommender systems are a way to combine the support of library user interaction with information access beyond catalog searches. Keywords: Recommender system, rating service, review service, mech￾anism design, incentive system. 1 Introduction The general public is lately becoming accustomed with recommender systems of different kinds at various online stores. But scientific libraries, where the profit contribution of a product (library document) is not the first concern and the costumers (library users) are coming due to very different incentives, are defini￾tively a not less promising application area. Due to the supply complexity or the evaluation of the quality, scientists and students are more and more inca￾pable of efficiently finding relevant literature in conventional database oriented catalog systems and search engines. A common solution to this problem lies in asking peers (see e. g. [10]). Recommender systems aggregate knowledge from many peer groups to the level of expert advice services. They bear the poten￾tial to significantly reduce transaction costs for literature searches by means of their aggregation capabilities. Scientific libraries are in a good strategic posi￾tion to become (even more than now) the information centers of the future [7]. Turning library online public access catalogs (OPAC) into customer oriented service portals supporting the interaction of the customers is one step to this L. Kov´acs, N. Fuhr, and C. Meghini (Eds.): ECDL 2007, LNCS 4675, pp. 428–439, 2007. c Springer-Verlag Berlin Heidelberg 2007