my VU: A Next Generation Recommender System plement fitness-proportional selection operators of the evolutionary algorithm and"discover services"which implement mutation operators of the evolution ary algorithm. Favorite Entries shown in Figure 4 is an example of such a rec- ommender service. Note, that a user interface element may consist of a bundle According to Holland(1975) fitness-biased selection operators serve the ex- ploitation of information. In my VU fitness-biased selection operators produce lists of information products ranked by their fitness. Ranking information products has a strong influence on the choice behavior of users. This fact has been established by several studies of user choice behavior in online environments. For a survey,see Introna and Nissenbaum(2000). In my vU, fitness-biased selection operators make repeat-buying behavior easier and thus increase user convenience. Convenience for repeat-buying has been identified as one of the key success factors for e-commerce sites by Bellmann et al. (1999) Mutation operators have the role of supporting the exploration of informatic my VU mutation operators either have the form of randomly generated link or category lists or of a randomly drawn banner leading to an information product in the virtual university. In the current implementation of my vU the mutation opera tors draw from the list of all information products ever purchased by some user and from the list of all information product categories ever used by some user. However leases of my VU additional mutation oper awing from other neigh- borhoods(e.g. all information products and categories in the virtual university, the conditional probability distribution of cross-selling,.)will be investigated. Muta- tion addresses an incentive problem of recommender systems discussed in Resnick and Varian(1997), namely, that users receiving recommendations diminish their search effort for information products and increasingly rely on a very narrow set of information products Consider, for example the Favorite Entries recommendation service illustrated in Figure 4. The link list is ranked according the the user's personal purchase fre quencies computed from his purchase history. Klicking on the"link"(the recom- mender service) with the label others also use in the line below the link Genetische Lernverfahren"(Genetic Machine Learning) leads to a list of informa- tion products y1, .. Un ranked according to the conditional probability P(y; l "Genetische Lernverfahren") The"link"(the recommender service)with the label: experts also use in- dicates that the user is an expert in the field of Genetic Machine Learning and it leads to a list of information products of the same category ranked according to the probability of being purchased by other experts for this category Recommendations based on the experience level of users for a category of in- formation products are only available for users who have revealed their experience for this category. This is a tit-for-tat strategy which adresses the free-riding problem inherent in recommender systems and it offers an incentive to the user to reveal his self-assessment of his experience for a category of information products. In the fu ture, we expect to exploit this information for learning progress monitoring and for team-buildingmyVU: A Next Generation Recommender System 7 plement fitness-proportional selection operators of the evolutionary algorithm and “discover services” which implement mutation operators of the evolution￾ary algorithm. Favorite Entries shown in Figure 4 is an example of such a rec￾ommender service. Note, that a user interface element may consist of a bundle of such services. According to Holland (1975) fitness-biased selection operators serve the ex￾ploitation of information. In myVU fitness-biased selection operators produce lists of information products ranked by their fitness. Ranking information products has a strong influence on the choice behavior of users. This fact has been established by several studies of user choice behavior in online environments. For a survey, see Introna and Nissenbaum (2000). In myVU, fitness-biased selection operators make repeat-buying behavior easier and thus increase user convenience. Convenience for repeat-buying has been identified as one of the key success factors for e-commerce sites by Bellmann et al. (1999). Mutation operators have the role of supporting the exploration of information. In myVU mutation operators either have the form of randomly generated link or category lists or of a randomly drawn banner leading to an information product in the virtual university. In the current implementation of myVU the mutation opera￾tors draw from the list of all information products ever purchased by some user and from the list of all information product categories ever used by some user. However, in future releases of myVU additional mutation operators drawing from other neigh￾borhoods (e.g. all information products and categories in the virtual university, the conditional probability distribution of cross-selling, ...) will be investigated. Muta￾tion addresses an incentive problem of recommender systems discussed in Resnick and Varian (1997), namely, that users receiving recommendations diminish their search effort for information products and increasingly rely on a very narrow set of information products. Consider, for example the Favorite Entries recommendation service illustrated in Figure 4. The link list is ranked according the the user’s personal purchase fre￾quencies computed from his purchase history. Klicking on the “link” (the recom￾mender service) with the label ::others also use in the line below the link “Genetische Lernverfahren” (Genetic Machine Learning) leads to a list of informa￾tion products ✂￾✍✆✠✞✠✞✟✞✥✆ ✂✏✎ ranked according to the conditional probability ✑✓✒☛✂✍✕✗✖ “Genetische Lernverfahren”✘ . The “link” (the recommender service) with the label ::experts also use in￾dicates that the user is an expert in the field of Genetic Machine Learning and it leads to a list of information products of the same category ranked according to the probability of being purchased by other experts for this category. Recommendations based on the experience level of users for a category of in￾formation products are only available for users who have revealed their experience for this category. This is a tit-for-tat strategy which adresses the free-riding problem inherent in recommender systems and it offers an incentive to the user to reveal his self-assessment of his experience for a category of information products. In the fu￾ture, we expect to exploit this information for learning progress monitoring and for team-building