my VU: A Next Generation Recommender System Observatic Transaction Aggregation Agents Statistics Services Fig 3. my VU as interactive evolutionary algorithm 2. By following(2)one of these links the user reveals his preference for this web site. The oberservation agents record (4)the purchase incident in the transaction log. The fitness of an information product is proportional to the number of time it has been purchased. In my vU the fitness of an information product is thus not explicitely assigned by a user, it is computed from observed user behavior 3. By incrementally revealing their experience(novice, average, advanced or ex pert) with information product categories they have visited in previous ses- sions, my VU users establish(3)their own experience profile which is used for computing group specific recommendations(6)and for selecting the appropr ate group specific recommender service(7)for the user. This mechanism ad dresses the problem of heterogeneous user groups and has been recommended in Shapiro and Varian(1999) 4. Every night aggregation agents update the retail statistics with the purchase incidents(5)from the transaction log in accordance with the experience level of the user(6). Today, practitioners call this"web-mining. Such statistics include depending on the degree of anonymity chosen by the user) (a)If we can extract anonymous user sessions from the transaction log, market baskets can be analyzed. We compute for each information product yi the frequencies that, if information product yi is in a market basket, the other information products y1, .. i-1, yi+1,. yn are also in the basket. This requency is proportional to the conditional probability P(yiyi) to buy information product yi, if information product yi has been purchased in the same session (b)If we can extract pseudonymous user sessions from the transaction log, we can establish the purchase histories of users and combine these with their experience profiles. (Note, that pseudonymous means, that we do not know the true identity of a user, we only know that it is the same user. )We com- pute for all information products in a category the frequencies with which they are bought by a novice, an average user, an advanced user, and anmyVU: A Next Generation Recommender System 5 Observation Agents Experience Profile Recommendation Agents User (Fitness Function) Transaction Log Aggregation Agents Retail Recommender Statistics Services (Biased Selection) User Interface Discover Services (Mutation) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Fig. 3. myVU as interactive evolutionary algorithm. 2. By following (2) one of these links the user reveals his preference for this web￾site. The oberservation agents record (4) the purchase incident in the transaction log. The fitness of an information product is proportional to the number of times it has been purchased. In myVU the fitness of an information product is thus not explicitely assigned by a user, it is computed from observed user behavior. 3. By incrementally revealing their experience (novice, average, advanced or ex￾pert) with information product categories they have visited in previous ses￾sions, myVU users establish (3) their own experience profile which is used for computing group specific recommendations (6) and for selecting the appropri￾ate group specific recommender service (7) for the user. This mechanism ad￾dresses the problem of heterogeneous user groups and has been recommended in Shapiro and Varian (1999). 4. Every night aggregation agents update the retail statistics with the purchase incidents (5) from the transaction log in accordance with the experience level of the user (6). Today, practitioners call this “web-mining”. Such statistics include (depending on the degree of anonymity chosen by the user): (a) If we can extract anonymous user sessions from the transaction log, market baskets can be analyzed. We compute for each information product ✂☎✄ the frequencies that, if information product ✂☎✄ is in a market basket, the other information products ✂￾✝✆✟✞✠✞✟✞✠✆ ✂✄☛✡ ￾✝✆ ✂✄✌☞ ￾✍✆✠✞✟✞✠✞ ✂✏✎ are also in the basket. This frequency is proportional to the conditional probability ✑✓✒✔✂✝✕✗✖ ✂✄✙✘ to buy information product ✂✝✕ , if information product ✂✄ has been purchased in the same session. (b) If we can extract pseudonymous user sessions from the transaction log, we can establish the purchase histories of users and combine these with their experience profiles. (Note, that pseudonymous means, that we do not know the true identity of a user, we only know that it is the same user.) We com￾pute for all information products in a category the frequencies with which they are bought by a novice, an average user, an advanced user, and an