Paper Info
Title
Diversity-conscious retrieval from generalized cases: a branch and bound algorithm
Abstract
Recommendation systems offer the most similar point cases to a target query. Among those cases similar to the query, some may be similar and others dissimilar to each other. Offering only the most similar cases wrt. the query leads to the well known problem that the customers may have only a few number of choices. To address the problem of offering a diverse set of cases, several approaches have been proposed. In a different line of CBR research, the concept of generalized cases has been systematically studied, which can be applied to represent parameterizable products. First approaches to retrieving the most similar point cases from a case base of generalized cases have been proposed. However, until now no algorithm is known to retrieve a diverse set of point cases from a case base of generalized cases. This is the topic of this paper. We present a new branch and bound method to build a retrieval set of point cases such that its diversity is sufficient and each case in the retrieval set is a representative for a set of similar point cases.
Year
DOI
Venue
2003
10.1007/3-540-45006-8_26
ICCBR
Keywords
Field
DocType
similar cases wrt,bound method,generalized case,case base,point case,cbr research,target query,retrieval set,similar point case,diversity-conscious retrieval,bound algorithm,diverse set,recommender system,branch and bound algorithm
Recommender system,Data mining,Branch and bound,Computer science,Case base,Artificial intelligence,Branch and bound method,Case-based reasoning,Machine learning
Conference
Volume
ISSN
ISBN
2689
0302-9743
3-540-40433-3
Citations 
PageRank 
References 
4
0.43
4
Authors
5
Name
Order
Citations
PageRank
Babak Mougouie1686.01
Michael M. Richter2471102.15
Ralph Bergmann31291139.44
Kevin D. Ashley41427201.23
Derek G. Bridge585073.07