Cloud-native application programming interface (api) recommendation method fusing data augmentation and contrastive learning

Patent metadata

Field	Value
Publication number	US-2025013514-A1
Application number	US-202318461528-A
Country	US
Kind code	A1
Filing date	Sep 6, 2023
Priority date	Jul 5, 2023
Publication date	Jan 9, 2025
Grant date	—

Abstract

Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

First claim

What is claimed is:

1 . A cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning, comprising:
step 1, on the basis of service invocation matrix information and function association information between APIs, constructing an invocation graph structure, service invocation matrix graph (SIMG), between mashup services and API services and an invocation graph structure, connecting API functional graph (CAFG) between the APIs to form a double-graph structure; step 2, according to the invocation graph structure SIMG, designing a multi-layer graph propagation network structure to learn mashup feature representations and API feature representations; step 3, according to the invocation graph structure CAFG, designing a corresponding graph neural network structure to compute feature representations corresponding to the APIs under the corresponding graph neural network structure; step 4, designing a mutual attention mechanism layer to retain multi-layer output information, and computing attention weights of multi-layer output and merging feature representations of the mashup services by taking association information of the APIs as importance guidance; step 5, carrying out weighted combination on the API feature representations of double graphs through a gate mechanism to generate a new API feature representation; step 6, computing training similarity and prior information similarity between services, and generating data augmented sequence pairs through a data augmentation method; step 7, computing pair-wise scores through the obtained feature representation, and computing an overall loss function result through the pair-wise scores and the data augmented sequence pairs to optimize parameters of an overall recommendation model; and step 8, matching a user request, sorting the pair-wise scores and completing service recommendation.

2 . The cloud-native API recommendation method fusing data augmentation and contrastive learning according to claim 1 , wherein step 1 comprises:
step 1.1 representing the mashup services in a mashup service set M as a mashup type of graph nodes, representing the APIs in an API set A as an API type of the graph nodes, and representing an overall graph node set V mα ; step 1.2 setting an empty set ε mα as a storage set of side information in the invocation graph structure regarding SIMG; step 1.3 traversing the mashup services in the mashup service set M, setting a single mashup service m traversed currently, and obtaining an API set m invoke invoked by the mashup service set M from an invocation interaction matrix MA between the mashups and the APIs, wherein the invocation interaction matrix MA has a size of |M|×|A|, wherein |M| indicates the number of elements in the mashup service set M, and |A| indicates the number of elements in the API set A; step 1.4 traversing the API set m invoke obtained in step 1.3, setting the APIs a traversed currently, and with when a value corresponding to a corresponding position [m, α] in the invocation interaction matrix MA is 1, which indicates that an invocation relation exists between the single mashup service m and the APIs α, storing a side combination (m, α) into the empty set ε mα , which indicates that an undirected connection side exists between the graph nodes corresponding to m and α; step 1.5 completing traversal, and combining the overall graph node set V mα and the empty set ε mα to complete construction of a graph structure SIMG((V mα , ε mα ); step 1.6 converting the API set A into a graph node set V α ; step 1.7 setting a side information set ε α as a storage set of side information in the graph structure CAFG; step 1.8 traversing the APIs α in the API set A, setting a single API α 1 traversed currently, on this basis, traversing the APIs α in the API set A apart from the single API α 1 , and setting a single API α 2 traversed currently; step 1.9 with a tag information set A tag of the APIs α comprising tags corresponding to the APIs α, setting a tag set A α1 tag corresponding to the single API α 1 , setting a tag set A α2 tag corresponding to the single API α2 and when A α1 tag ∩A α2 tag is not the empty set ε mα storing a side combination (α 1 , α 2 ) into the side information set ε α , which indicates that an undirected connection side exists between graph nodes corresponding to the single API α i and the single API α 2 ; and step 1.10 completing traversal, and combining the graph node set V α and the side information set ε α to complete construction of a graph structure CAFG(V α , ε α ).

3 . The cloud-native API recommendation method fusing data augmentation and contrastive learning according to claim 2 , wherein the step 2 comprises:
step 2.1 constructing an embedding layer to store embedding vector information of the mashup services and API nodes in forms of:

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Assignees

• Univ Jiliang China

Inventors

• Lu Jiawei
• Xiao Gang
• Wang Qibing
• Zheng Jiahong
• Yu Dongjin
• Zhang Xudong
• Chen Zhenbo
• Xu Jun
• Li Duanni
• Wang Cece

Classifications

• G06F17/16 Primary

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0895

  Weakly supervised learning, e.g. semi-supervised or self-supervised learning · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06F9/547 Primary

  Remote procedure calls [RPC]; Web services · CPC title