A.W. Neumann Rezension schreiben Bewertung abgeben Rezensionen anzeige Meine Rezensionen Empfehlungen Bewertung des Titels nach 食★★★★ Studenten:35(4Bew) ★☆★★ o Mtarbeiter:445Bew Fig 1. Recommender start interface on a document's detailed inspection page sion schreiben- Write review: Bewertung abgeben- Submit rating: Rezensionen nzeigen-Inspect reviews: Meine Rezensionen- My reviews: Empfehlungen mendations: Bewertung des Titels nach Nutzergruppen- Ratings of the titles by user group the principle of self-selection [17, 22 In a library setting usage behavior can be observed at different stages: detailed inspections of documents in the OPAc ordering paper documents from the magazine, ordering paper documents that are currently lent, and finally picking-up a paper document or downloading a file from the digital library. The main concern for the data selection is bias. It can be shown that lending and ordering data is highly biased, since e.g. stu- dents very often do not order the book they are mostly interested in, because most likely it is already lent, but actually their consideration-set only includes documents that they will be able to obtain timely before the corresponding ex amination. In marketing several conceptual models which describe a sequence of sets(e.g. total set 2 awareness set 2 consideration set 2 choice set(11 p. 153)) have been developed to describe such situations [14, 23. For this reason the behavior-based recommender service at the University Library of Karlsruhe based on anonymized OPAC searches(hits on document inspection pages)and ot on lending data. Due to transaction costs the detailed inspection of docu- ments in the OPAC of a library can be put on a par with a purchase incidence in a consumer store setting. A market basket consists of all documents that have been co-inspected by one anonymous user within one session. To answer the question, which co-inspections occur non-random, an algorithm based on calculating inspection frequency distribution functions following a logarithmic series distribution(LSD)is applied 6. Such a recommender system is opera- tional at the OPac of the University Library of Karlsruhe in a first version since430 A.W. Neumann Fig. 1. Recommender start interface on a document’s detailed inspection page. Rezen￾sion schreiben – Write review; Bewertung abgeben – Submit rating; Rezensionen anzeigen – Inspect reviews; Meine Rezensionen – My reviews; Empfehlungen – Recom￾mendations; Bewertung des Titels nach Nutzergruppen – Ratings of the titles by user group. the principle of self-selection [17,22]. In a library setting usage behavior can be observed at different stages: detailed inspections of documents in the OPAC, ordering paper documents from the magazine, ordering paper documents that are currently lent, and finally picking-up a paper document or downloading a file from the digital library. The main concern for the data selection is bias. It can be shown that lending and ordering data is highly biased, since e.g. stu￾dents very often do not order the book they are mostly interested in, because most likely it is already lent, but actually their consideration-set only includes documents that they will be able to obtain timely before the corresponding ex￾amination. In marketing several conceptual models which describe a sequence of sets (e. g. total set ⊇ awareness set ⊇ consideration set ⊇ choice set ([11], p. 153)) have been developed to describe such situations [14,23]. For this reason the behavior-based recommender service at the University Library of Karlsruhe is based on anonymized OPAC searches (hits on document inspection pages) and not on lending data. Due to transaction costs the detailed inspection of docu￾ments in the OPAC of a library can be put on a par with a purchase incidence in a consumer store setting. A market basket consists of all documents that have been co-inspected by one anonymous user within one session. To answer the question, which co-inspections occur non-random, an algorithm based on calculating inspection frequency distribution functions following a logarithmic series distribution (LSD) is applied [6]. Such a recommender system is opera￾tional at the OPAC of the University Library of Karlsruhe in a first version since