Motivating and Supporting User Interaction with Recommender Systems 429 goal. Valid and credible information is a scarce resource[ 20. Information con- sumes the attention of its recipients. Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it. "211 The more general term"recommender system"was coined by Resnick and Varian to better describe the action than the more narrow " collaborative fil- tering"16. A recommender system reads observed user behavior or opinions from users as input, then aggregates and directs the resulting recommendations to appropriate recipients. Recommender systems can be classified into two dif- ferent main categories. An implicit recommender system is based on behavioral usage data like purchases, digital library catalog inspections, or lending data An explicit recommender system directly asks the users for their opinions on certain objects. A more technical classification with a focus on applications in e-commerce can be found in [18 and [ 19. For a more up-to-date overview on recommender systems e. g. see Adomavicius and Tuzhilin [1]. In Geyer-Schulz et al. 5 an early application of recommender systems including group-specific services in e-learning is presented. Herlocker et al. 9 deals with the technical evaluation of recommender systems The focus of this paper lies on the experiences with motivation and support of interaction between library users at the University Library of Karlsruhe. First the introduced recommender systems are described, then mechanism design is discussed to address motivational problems. Finally, general lessons learned from integrating different recommender systems into large existing legacy library ap- plications are summarized and the evaluation of such systems is discussed All in this paper presented recommender systems are fully operational services accessible by the general public. For further information on how to use these see "parTicipate!"athttp://reckvk.em.uni-karlsruhe.de/.Inanswertostrong privacy concerns among students and scientists all portrayed recommender ser- vices are object-centered. They do not classify the users by observation or asking them for their interest, but they classify and gather data on the documents of a library. Figure 1 shows a cutout of the detailed document inspection page of [13] in the OPac of the University Library of Karlsruhe. The behavior-based ser vice is accessibly by clicking on "Empfehlungen"(Recommendations), the rating service by "Bewertung abgeben"(Submit rating) or direct inspection of"Bew ertung des Titels nach Nutzergruppen"(Ratings of the titles by user group) and finally the review service by "Rezension schreiben"(Write review),"Rezen- sionen anzeigen"(Inspect reviews), and"Meine Rezensionen"(My reviews). All systems are programmed in Perl or PHP(or a combination of both), use Post- greSQL databases, and are running on Linux servers 2 Behavior-Based Recommender service Behavior-based recommender services are observing the behavior of users and thereby implicitly collecting information about the objects the users are inspect g. The necessary homogeneity of a group of users in this case is granted byMotivating and Supporting User Interaction with Recommender Systems 429 goal. Valid and credible information is a scarce resource [20]. Information con￾sumes the attention of its recipients. “Hence a wealth of information creates a poverty of attention and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.” [21] The more general term “recommender system” was coined by Resnick and Varian to better describe the action than the more narrow “collaborative fil￾tering” [16]. A recommender system reads observed user behavior or opinions from users as input, then aggregates and directs the resulting recommendations to appropriate recipients. Recommender systems can be classified into two dif￾ferent main categories. An implicit recommender system is based on behavioral usage data like purchases, digital library catalog inspections, or lending data. An explicit recommender system directly asks the users for their opinions on certain objects. A more technical classification with a focus on applications in e-commerce can be found in [18] and [19]. For a more up-to-date overview on recommender systems e. g. see Adomavicius and Tuzhilin [1]. In Geyer-Schulz et al. [5] an early application of recommender systems including group-specific services in e-learning is presented. Herlocker et al. [9] deals with the technical evaluation of recommender systems. The focus of this paper lies on the experiences with motivation and support of interaction between library users at the University Library of Karlsruhe. First, the introduced recommender systems are described, then mechanism design is discussed to address motivational problems. Finally, general lessons learned from integrating different recommender systems into large existing legacy library ap￾plications are summarized and the evaluation of such systems is discussed. All in this paper presented recommender systems are fully operational services accessible by the general public. For further information on how to use these see “Participate!” at http://reckvk.em.uni-karlsruhe.de/. In answer to strong privacy concerns among students and scientists all portrayed recommender ser￾vices are object-centered. They do not classify the users by observation or asking them for their interest, but they classify and gather data on the documents of a library. Figure 1 shows a cutout of the detailed document inspection page of [13] in the OPAC of the University Library of Karlsruhe. The behavior-based ser￾vice is accessibly by clicking on “Empfehlungen” (Recommendations), the rating service by “Bewertung abgeben” (Submit rating) or direct inspection of “Bew￾ertung des Titels nach Nutzergruppen” (Ratings of the titles by user group), and finally the review service by “Rezension schreiben” (Write review), “Rezen￾sionen anzeigen” (Inspect reviews), and “Meine Rezensionen” (My reviews). All systems are programmed in Perl or PHP (or a combination of both), use Post￾greSQL databases, and are running on Linux servers. 2 Behavior-Based Recommender Service Behavior-based recommender services are observing the behavior of users and thereby implicitly collecting information about the objects the users are inspect￾ing. The necessary homogeneity of a group of users in this case is granted by