Motivating and Supporting User Interaction with Recommender Systems 437 as information access beyond catalog searches. Recommender systems are a way to combine both. Different recommender systems support different user needs (e. g. finding standard literature or finding a specialized document for a specific topic). To amplify the described services the derived information is going to be stronger connected in the future. On one hand, e. g. the rating data can be used to further filter the behavior-based recommendations, on the other hand a different graph-based visualization approach that portrays the heterogeneous data from the different systems within one view is developed. Another way is a market-based approach to decide which information from which system should be offered to the user. The principle design of such a marketplace is described All presented recommender systems are becoming regular OPAC features at e University Library of Karlsruhe. The introduction of the implicit recom- mender services is conducted in several steps. The first step comprised the te nical development and launch of the services in the form described by this paper To measure the intrinsic motivation of the users and to find the main obstacles for the users within the system, no technical incentive system like user point accounts was included, neither were any users directly asked to write reviews or give ratings. Although a lot of positive feedback for the systems itself was received, the free-riding problem can be hold responsible for the overall low in formation users have been put into the system. To overcome this situation, in the next steps the following is planned. First, students will be asked to write reviews on literature they are using for seminars to increase the number of quality r views. Second, a reputation systems(list of best reviewers, best reviews, etc )will be included and will be accompanied at an even later stage by a compensation system to raise extrinsic motivation Throughout all steps the evaluation of the quality of the ratings and reviews are of concern as well. Currently, the quality of reviews is measured by the rat ings of reviews. No objective metric exists to measure the quality of scientifie documents in an absolute way, the metric always depends on the function document has to fulfill for a specific user. Once a reasonable number of sub- missions of ratings of documents exists, these ratings could be compared with data from other systems like Amazon. com, data from citation indices might correlate with ratings from scientists, and an evaluation by experts (lectur- ers, librarians, etc. )could lead to further insights as well. The most reasonable way to measure the effectiveness of the systems lies in observing the usage of the systems and asking the users, if the recommender systems(and thereby other users) helped them to find the right literature for the task they had in mn Acknowledgments. The author gratefully acknowledge the funding of the project "Recommender Systems for Meta Library Catalogs" by the Deutsche ForschungsgemeinschaftMotivating and Supporting User Interaction with Recommender Systems 437 as information access beyond catalog searches. Recommender systems are a way to combine both. Different recommender systems support different user needs (e. g. finding standard literature or finding a specialized document for a specific topic). To amplify the described services the derived information is going to be stronger connected in the future. On one hand, e. g. the rating data can be used to further filter the behavior-based recommendations, on the other hand a different graph-based visualization approach that portrays the heterogeneous data from the different systems within one view is developed. Another way is a market-based approach to decide which information from which system should be offered to the user. The principle design of such a marketplace is described in [24]. All presented recommender systems are becoming regular OPAC features at the University Library of Karlsruhe. The introduction of the implicit recom￾mender services is conducted in several steps. The first step comprised the tech￾nical development and launch of the services in the form described by this paper. To measure the intrinsic motivation of the users and to find the main obstacles for the users within the system, no technical incentive system like user point accounts was included, neither were any users directly asked to write reviews or give ratings. Although a lot of positive feedback for the systems itself was received, the free-riding problem can be hold responsible for the overall low in￾formation users have been put into the system. To overcome this situation, in the next steps the following is planned. First, students will be asked to write reviews on literature they are using for seminars to increase the number of quality re￾views. Second, a reputation systems (list of best reviewers, best reviews, etc.) will be included and will be accompanied at an even later stage by a compensation system to raise extrinsic motivation. Throughout all steps the evaluation of the quality of the ratings and reviews are of concern as well. Currently, the quality of reviews is measured by the rat￾ings of reviews. No objective metric exists to measure the quality of scientific documents in an absolute way, the metric always depends on the function a document has to fulfill for a specific user. Once a reasonable number of sub￾missions of ratings of documents exists, these ratings could be compared with data from other systems like Amazon.com, data from citation indices might correlate with ratings from scientists, and an evaluation by experts (lectur￾ers, librarians, etc.) could lead to further insights as well. The most reasonable way to measure the effectiveness of the systems lies in observing the usage of the systems and asking the users, if the recommender systems (and thereby other users) helped them to find the right literature for the task they had in mind. Acknowledgments. The author gratefully acknowledge the funding of the project “Recommender Systems for Meta Library Catalogs” by the Deutsche Forschungsgemeinschaft