# A Review of the Development and Future Challenges of Case-Based Reasoning
## 3 Development of CBR Key Technologies
### Similarity Measure and Case Retrieval
#### 3.2.2 Case Retrieval  
As an important step of case-based reasoning, obtaining the correct case retrieval results determines the overall performance of CBR systems. Effective retrieval means not only finding similar cases but also finding useful similar cases. To date, many mature retrieval methods have been formed, including the nearest neighbor algorithm (NN) [49], knowledge-guided approach [50], template retrieval [51], two-level retrieval [52], and index-based retrieval [53], etc. The efficiency of these retrieval methods depends to a large extent on the following:  
* The representation of the objects.
* The case base structure.
* The similarity measure.
* The accuracy of the intended answer or solution.  
In case retrieval, similarity-based retrieval methods [35; 36; 54] are the most interesting to scholars and have been widely used. The classic representative is NN (KNN), which is the most commonly used method in case retrieval because its principle is easy to understand and simple to calculate. However, for complex large-scale data, the simplicity of the method also limits its retrieval in complex cases; therefore, NN is often combined with other algorithms to improve retrieval accuracy and efficiency [55; 56; 57]. Although purely similarity-based retrieval is still the most widely used technology, the limitations of similarity are gradually exposed with the development of CBR; for example, the results obtained by retrieval are singular and cannot provide users with more novel and valuable references. There may be no similar cases or only a few cases with similar features in the case base; this leads to a no-retrieval result. Alternatively, the retrieval results cannot provide support for subsequent re-use stages; therefore, while similarity continues to play a role in retrieval, it also gradually combines with other standards to guide the retrieval process.  
1. Retrieval based on diversity is often used in recommendation systems to provide users with more diversified solutions to avoid similar recommended single and limited cases [58; 59].
2. Retrieval oriented to adaptation guidance [60] is based on the assumption that the most similar situation may not be adapted and can perform preliminary adaptation work during the retrieval process, which can significantly reduce adaptation failure and adaptation costs [61; 62].
3. Interpretation-oriented retrieval, which explains CBR and justifies recommendations or solutions, is often important, especially in the field of medical decision making, in which explaining the cause and correctness of the search results can provide compelling support [63; 64; 65].  
In addition, selecting different retrieval methods according to case representation is also a key research direction for many scholars; for example, text language retrieval combined with natural language processing (NLP) technology [66; 67], retrieval based on non-symbolic types such as images [68], or case retrieval with missing case information [69]. These methods mainly aim at the analysis of the features of the case data itself, combining feature selection and measurement algorithms to design the case retrieval method.