Title
A recommender mechanism based on case-based reasoning
Abstract
Case-based reasoning (CBR) algorithm is particularly suitable for solving ill-defined and unstructured decision-making problems in many different areas. The traditional CBR algorithm, however, is inappropriate to deal with complicated problems and therefore needs to be further revised. This study thus proposes a next-generation CBR (GCBR) model and algorithm. GCBR presents as a new problem-solving paradigm that is a case-based recommender mechanism for assisting decision making. GCBR can resolve decision-making problems by using hierarchical criteria architecture (HCA) problem representation which involves multiple decision objectives on each level of hierarchical, multiple-level decision criteria, thereby enables decision makers to identify problems more precisely. Additionally, the proposed GCBR can also provide decision makers with series of cases in support of these multiple decision-making stages. GCBR furthermore employs a genetic algorithm in its implementation in order to reduce the effort involved in case evaluation. This study found experimentally that using GCBR for making travel-planning recommendations involved approximately 80% effort than traditional CBR, and therefore concluded that GCBR should be the next generation of case-based reasoning algorithms and can be applied to actual case-based recommender mechanism implementation.
Year
DOI
Venue
2012
10.1016/j.eswa.2011.09.161
Expert Syst. Appl.
Keywords
Field
DocType
traditional cbr algorithm,case-based reasoning algorithm,traditional cbr,multiple decision objective,genetic algorithm,proposed gcbr,next-generation cbr,multiple-level decision criterion,actual case-based recommender mechanism,decision maker,case based reasoning
Data mining,Architecture,Multiple-criteria decision analysis,Computer science,Artificial intelligence,Case-based reasoning,Genetic algorithm,Machine learning
Journal
Volume
Issue
ISSN
39
4
0957-4174
Citations 
PageRank 
References 
12
0.57
15
Authors
2
Name
Order
Citations
PageRank
Chen-Shu Wang17112.85
hengli yang234427.53