A.W. Neumann also aiding the selection of appropriate new users 15. On the other hand, when offering compensations, intrinsic motivation is often displaced by extrinsic moti- vation. So, once you offer compensations e. g. in form of free book donations to the best reviewers, you scare away some users, that were willing to contribut out of altruism or their implicit membership to the scientific community before Unfortunately, it has been shown that experiments to measure these motivations correctly are very hard to accomplish 12 In e-commerce applications shilling of recommender systems is often a mo tivation. The possibility to submit anonymously (or with fake accounts only requiring an e-mail address) ratings and reviews for one's own products to boost sales leads to significantly more submissions. This mechanism is less dominant in a library setting. The more restrictive the submission process is handled, the less submissions can be expected. The recommender systems at the University Library of Karlsruhe in the current first implementation are very restrictive in the area of the accepted user group and the anonymity towards the system ad ministrator Lessening the restrictions may lead to more submissions with the drawback of a higher rate of biased ratings and reviews 3. In general, mechanism design problems are of less concern with behavior systems. ng consciously in a well implemented system(including web robot prevention) has very high transaction costs and therefore is mostly unattractive in a library setting, and finally all users of the OPAC (regardless of their interest in rec- ommendations) contribute to the recommender system and thereby helping to scale it up. Achieving the critical mass is the most important goal for stand- alone recommender systems(cold start problem) but is less indispensable to life for systems that are placed as value-added services to already high frequented information centers like digital library OPACs. The credibility in the academic environment comes to a large part from the institution to which the library be- longs. If promotions or advertisements of any kind within the OPAc exist, a user should perceive a clear separation between these and the recommender system This is often not the case in e-commerce applications like e. g. Amazon. com, where products with a high contribution to profit are placed by product man- gers next to real recommendations from other customers. Recognition of good cooperation within explicit recommender systems can be measured by reputa tion systems(for credence goods e. g. see 3). A user point account tracks useful behavior(credit)and undesirable behavior(deduction of points). To keep users motivated an automatic discounting(decrease of points) over time is necessary The quality of a review e.g. can be measured by the ratings of other users for this review 5 Conclusions and Further research Scientific libraries hold a good strategic position to become digital information centers. Such information centers need to support library user interaction as well436 A.W. Neumann also aiding the selection of appropriate new users [15]. On the other hand, when offering compensations, intrinsic motivation is often displaced by extrinsic moti￾vation. So, once you offer compensations e. g. in form of free book donations to the best reviewers, you scare away some users, that were willing to contribute out of altruism or their implicit membership to the scientific community before. Unfortunately, it has been shown that experiments to measure these motivations correctly are very hard to accomplish [12]. In e-commerce applications shilling of recommender systems is often a mo￾tivation. The possibility to submit anonymously (or with fake accounts only requiring an e-mail address) ratings and reviews for one’s own products to boost sales leads to significantly more submissions. This mechanism is less dominant in a library setting. The more restrictive the submission process is handled, the less submissions can be expected. The recommender systems at the University Library of Karlsruhe in the current first implementation are very restrictive in the area of the accepted user group and the anonymity towards the system ad￾ministrator. Lessening the restrictions may lead to more submissions with the drawback of a higher rate of biased ratings and reviews. In general, mechanism design problems are of less concern with behavior￾based recommender systems. Free-riding is almost not possible, to create bias consciously in a well implemented system (including web robot prevention) has very high transaction costs and therefore is mostly unattractive in a library setting, and finally all users of the OPAC (regardless of their interest in rec￾ommendations) contribute to the recommender system and thereby helping to scale it up. Achieving the critical mass is the most important goal for stand￾alone recommender systems (cold start problem) but is less indispensable to life for systems that are placed as value-added services to already high frequented information centers like digital library OPACs. The credibility in the academic environment comes to a large part from the institution to which the library be￾longs. If promotions or advertisements of any kind within the OPAC exist, a user should perceive a clear separation between these and the recommender system. This is often not the case in e-commerce applications like e. g. Amazon.com, where products with a high contribution to profit are placed by product man￾agers next to real recommendations from other customers. Recognition of good cooperation within explicit recommender systems can be measured by reputa￾tion systems (for credence goods e. g. see [3]). A user point account tracks useful behavior (credit) and undesirable behavior (deduction of points). To keep users motivated an automatic discounting (decrease of points) over time is necessary. The quality of a review e. g. can be measured by the ratings of other users for this review. 5 Conclusions and Further Research Scientific libraries hold a good strategic position to become digital information centers. Such information centers need to support library user interaction as well