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| Approach | Category | Pros | Cons |
|
| Service discovery based on DAML-S [8] | Open API discovery | The earliest service described by DARPA agent markup language | The randomness of user demand descriptions and service description texts leads to unsatisfactory results |
| Service discovery based on text mining [19] | It combines text mining and metaprogramming techniques | The approach is unable to mine deep relationships |
| Web service discovery based on an ontology [20, 22, 24] | They address the issue of nonexplicit service description semantics that match a specific service request | The semantic extension is not enough |
| Web service discovery based on WSDL documents clustering [21] | Narrowing the search space and improving results | Each feature is not assigned its own weight |
| Web service discovery based on hierarchical clustering [23] | The vector space model improves the accuracy and efficiency | It does not take the semantics into consideration |
| Web service discovery based on SAWSDL-iMatcher [25] | Multiple matching strategy extensions via XQuery can effectively aggregate similar values | The approach is only useful in one specific domain, not effective in other domains |
|
| Open API recommendation based on topic model [26, 31] | Open API recommendation | The document probability distribution is obtained, and the distance is used to calculate the semantic distance | The topic model is not well trained |
| Web service recommendation based on collaborative filtering [27] | Collaborative filtering does not require specialized domain knowledge and can be easily modeled | Collaborative filtering cannot mine hidden information |
| Model-based recommendation [28] | The use of a generic hierarchical graph model can improve efficiency and effectiveness | This approach cannot get synthesis of multiple constraints for more personalized recommendation |
| Social-aware recommendation [29] | It can predict unobserved relationships | The matrix sparsity affects accuracy |
| Manifold-learning-based recommendation [30] | Mashup can use manifold sorting algorithm for better clustering | The approach cannot handle dynamically added services |
| Combining machine learning and distributed recommendation [32] | More accurate prediction | The approach ignores QoS |
|
| HyOASAM | Open API discovery and recommendation | HyOASAM can handle random description text and make accurate recommendations for unclear user needs description | The modeling process is a little more complicated than other approaches |
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