Motivating and Supporting User Interaction with Recommender Systems 431 nmen mit folgenden Titeln auger 2006 1. Hgh Performance Linux Clusters / Sloan, Joseph D, 2005. (16) 围园 verteilte Programmierung /Rauber, Thomas: RUnger, Gudula, 2000. (10)a E d parallel computing 4. Using MPl/ Gropp, D: Lusk, Ewing L; Skiellum, Anthony, 1999, (9) 7) 回回回回回回 9. Beowulf cluster computing with Linux/ Sterling, Thamas Lawrence, 2002.(6 11. Custern mit Hintergrundwssen /Hotho, Andreas, 2004,(6) 2. C und Linux/ Grafe, Martin, 2005. (3) Fig. 2. Recommendation list of"Cluster computing" by Bauke and Mertens. The num- ber of co-inspections is given in brackets after each title June 2002 8 and in the current web service version(facilitating WSDL, XML and SOAP) since January 2006 Figure 2 shows the recommendation list of"Cluster computing"by Bauke nd Mertens(cut-out from the web page). The number of co-inspections is given in brackets after each title. Documents with less than three co-inspections have been rated by the lsd test to be not significantly related to this book. Since the usage distribution of documents in nearly every library is highly skewed(newer documents, or documents to topics that interest a large part of the overall library users, in general are more requested), many recommendations will be generated for documents that are used often while seldom used documents have fewer or no recommendations. Recommendations are updated daily. Of the 929 637 doc uments in the catalog, 192 647 documents have lists with recommendations, a total of 2 843017 recommendations exist. Because of the skewness, the coverage of actual detailed document inspections is 74.9%(much higher than the cover age of the complete catalog). So the probability that recommendations exist for a document a user is currently interested in is 0.749(status of 2007-03-19).A user survey asking the library users"I consider the recommendation service in general "on a Likert scale from 1(very bad) to 5(very good) yielded a mean of 4. 1 from 484 votes between 2005-03-21 and 2006-03-06. This type of recom- mender service is best suited to users trying to find standard literature or further standard readings of a field corresponding to the document they are currently nspecting. Although it does not support the direct interaction(communication) between customers, everybody using the service profits from the actions of other library users An e-mail notification service was added at a later stage. Users with a library account receive an e-mail including a direct link to the recommendation page ifMotivating and Supporting User Interaction with Recommender Systems 431 Fig. 2. Recommendation list of “Cluster computing” by Bauke and Mertens. The num￾ber of co-inspections is given in brackets after each title. June 2002 [8] and in the current web service version (facilitating WSDL, XML and SOAP) since January 2006. Figure 2 shows the recommendation list of “Cluster computing” by Bauke and Mertens (cut-out from the web page). The number of co-inspections is given in brackets after each title. Documents with less than three co-inspections have been rated by the LSD test to be not significantly related to this book. Since the usage distribution of documents in nearly every library is highly skewed (newer documents, or documents to topics that interest a large part of the overall library users, in general are more requested), many recommendations will be generated for documents that are used often while seldom used documents have fewer or no recommendations. Recommendations are updated daily. Of the 929637 doc￾uments in the catalog, 192647 documents have lists with recommendations, a total of 2 843017 recommendations exist. Because of the skewness, the coverage of actual detailed document inspections is 74.9% (much higher than the cover￾age of the complete catalog). So the probability that recommendations exist for a document a user is currently interested in is 0.749 (status of 2007-03-19). A user survey asking the library users “I consider the recommendation service in general” on a Likert scale from 1 (very bad) to 5 (very good) yielded a mean of 4.1 from 484 votes between 2005-03-21 and 2006-03-06. This type of recom￾mender service is best suited to users trying to find standard literature or further standard readings of a field corresponding to the document they are currently inspecting. Although it does not support the direct interaction (communication) between customers, everybody using the service profits from the actions of other library users. An e-mail notification service was added at a later stage. Users with a library account receive an e-mail including a direct link to the recommendation page if