my VU: A Next Generation Recommender System Service Usage(absolute) Usage(relative 628 3801% Newswire 4.66% Favorite entrie 21.19% Discover Entries 74 448% Discover Categ 478% Personal informatic 4.24% Table 1. Usage of my VU Services(January 26th, 2000-March 13th, 2000) Discover Categories(:: discover categories in the sidebar of Figure 5)is similar to Discover Entries, only at the level of information product cate- gories. The rationale for this service is to encourage the exploration of informa tion product categories which are new for a my VUuser They are grouped under the heading> favorites in the sidebar of the my V main page shown in Figure 5. Note, that recommender services are embedded as la bels into all my VU services including the my vU global bookmark service ( book- marks) which my VU users can access from wherever they are on the Internet In addition, the service:: recommender under the heading> customize allows the user to change his level of experience for categories he has visited in a previous session in the virtual university n Table I we have listed the actual usage of my VU services in the first Six weeks of operation(from January 26th, 2000 to March 13th, 2000). We see that all recommender services are actually used by my VU users. Favorite Entries is the most popular recommender services. It accounts for 20 percent of my VU service usage. Both mutation-based Discover services account for 4 percent of my VU usage each. Although this is only a very preliminary result, this seems to indicate that the mutation-based Discover services stimulate user curiosity and lead to an increase in the exploration efforts of users. Informally(e-mails and chat), student response to my VU has been favorable. At the end of March 2000 more than 200 users were 5 Future Research As usual, a lot remains to be done. From the perspective of evolutionary computa- tion which we have emphasized in this article, the following problems merit further What is a useful concept for a crossover operator for web-site design? A long term and more detailed study of the use of recommender services inmyVU: A Next Generation Recommender System 9 Service Usage (absolute) Usage (relative) Bookmarks 628 38.01% Newswire 77 4.66% Favorite Entries 350 21.19% Favorite Categories 228 13.8% Discover Entries 74 4.48% Discover Categories 79 4.78% Recommender Profile 146 8.84% Personal Information 70 4.24% Total 1652 100% Table 1. Usage of myVU Services (January 26th, 2000 – March 13th, 2000) ✦ Discover Categories (::discover categories in the sidebar of Figure 5) is similar to Discover Entries, only at the level of information product cate￾gories. The rationale for this service is to encourage the exploration of informa￾tion product categories which are new for a myVU user. They are grouped under the heading > favorites in the sidebar of the myVU main page shown in Figure 5. Note, that recommender services are embedded as la￾bels into all myVU servicesincluding the myVU global bookmark service (::book￾marks) which myVU users can access from wherever they are on the Internet. In addition, the service ::recommender under the heading > customize allows the user to change his level of experience for categories he has visited in a previous session in the virtual university. In Table 1 we have listed the actual usage of myVU services in the first six weeks of operation (from January 26th, 2000 to March 13th, 2000). We see that all recommender services are actually used by myVU users. Favorite Entries is the most popular recommender services. It accounts for 20 percent of myVU service usage. Both mutation-based Discover services account for 4 percent of myVU usage each. Although this is only a very preliminary result, this seems to indicate that the mutation-based Discover services stimulate user curiosity and lead to an increase in the exploration efforts of users. Informally (e-mails and chat), student response to myVU has been favorable. At the end of March 2000 more than 200 users were registered myVU users. 5 Future Research As usual, a lot remains to be done. From the perspective of evolutionary computa￾tion which we have emphasized in this article, the following problems merit further investigation: ✦ What is a useful concept for a crossover operator for web-site design? ✦ A long term and more detailed study of the use of recommender services in myVU is required