Secondary recommendation method based on service complementary relation learning model for restful service

Patent metadata

Field	Value
Publication number	US-2025156704-A1
Application number	US-202418635011-A
Country	US
Kind code	A1
Filing date	Apr 15, 2024
Priority date	Nov 13, 2023
Publication date	May 15, 2025
Grant date	—

Abstract

Disclosed is a secondary recommendation method based on a service complementary relation learning model for a RESTful service. The method includes: firstly, establishing a service complementary relation learning model, and setting a service complementary relation rule; on the basis, extracting an initial service complementary relation, expanding the initial service complementary relation with service function similarity information, and creating a complementary relation graph structure; secondly, conducting representation learning on the complementary relation graph structure in combination with a mask graph attention mechanism, and obtaining embedded vectors of a RESTful service and a service function; then, computing a distance between the embedded vectors, and reducing the distance between the embedded vectors having a complementary relation with a hinge loss function; and finally, finding the RESTful service having the complementary relation according to user input, and conducting secondary service recommendation.

First claim

What is claimed is:

1 . A secondary recommendation method based on a service complementary relation learning model for a RESTful service, comprising the following steps:
step 1: creating a service complementary relation learning model, establishing a RESTful service invocation data set, setting a service complementary relation rule, and extracting an initial service complementary relation as follows: 1.1. enabling the service complementary relation learning model to be a deep learning model capable of learning a service complementary relation between data samples of the RESTful service and conducting secondary recommendation on the basis of the service complementary relation, which is represented by a symbol SCRM; 1.2. enabling the RESTful service invocation data set to be a collection of data samples related to a service, a service invocation sequence and a service combination, which is represented by a symbol C; 1.3. configuring the service complementary relation rule to describe related information of strength of a complementary relation between two RESTful services in SCRM; and 1.4. extracting the initial service complementary relation as follows: extracting the initial service complementary relation in C according to the service complementary relation rule formulated in step 1.3; step 2: expanding the initial service complementary relation extracted in step 1.4 with service function similarity information, and obtaining a function service complementary relation as follows: 2.1. using the service function similarity information as follows: determining that any two RESTful services are functionally similar if subordinate service functions of the services have the same item, wherein a degree of similarity is evaluated with service function similarity; and 2.2. expanding the initial service complementary relation as follows: finding functionally similar RESTful services in the RESTful service invocation data set so as to expand the initial service complementary relation, and obtaining the function service complementary relation; step 3: creating a complementary relation graph structure on the basis of the initial service complementary relation and the function service complementary relation as follows: 3.1. configuring the complementary relation graph structure to describe a directed weighted graph of the initial service complementary relation and the function service complementary relation in SCRM, which is defined as ACWG=<V, E, W>, wherein ACWG represents the complementary relation graph structure, V represents a node set, E represents a directed edge set, and W represents a weight matrix; and 3.2. creating the complementary relation graph structure as follows: transforming the initial service complementary relation and the function service complementary relation into nodes, directed edges and edge weights in the complementary relation graph structure; step 4: transforming the RESTful service into an embedded vector through representation learning, learning an attention coefficient from the complementary relation graph structure AWCG created in step 3 with a mask graph attention mechanism, and optimizing the embedded vector in combination with the attention coefficient as follows: 4.1. transforming a data sample into a low-dimensional vector representation through representation learning, and firstly transforming any RESTful service α into an embedded vector ε α having a dimension d through representation learning in SCRM, wherein d is a hyper-parameter representing a dimension; 4.2. learning the complementary relation graph structure in combination with the mask graph attention mechanism, and obtaining the attention coefficient; and 4.3. optimizing the embedded vector with the attention coefficient as follows: conducting weighted average processing on the embedded vector with an attention coefficient between a node and a neighbor node; step 5: computing vector distances of different RESTful services in SCRM, transforming service functions into embedded vectors, and computing vector distances of different service functions as follows: 5.1. obtaining the vector distance of the RESTful service as follows: mapping the RESTful service to a vector space through step 4, and determining a distance between different RESTful services in the vector space to be the vector distance of the RESTful service; 5.2. transforming the service function as follows: transforming the service function into the embedded vector through representation learning; and 5.3. computing the vector distance of the service function as follows: mapping the service function to the vector space through step 5.2, and determining a distance between different service functions in the vector space to be the vector distance of the service function; step 6: optimizing SCRM with a gradient descent algorithm and a hinge loss function, and reducing a vector distance between a RESTful service and a service function having a complementary relation, wherein the gradient descent algorithm is an optimization algorithm in deep learning and is configured to find a local minimum of a function, and the hinge loss function is a loss function commonly used in semi-supervised learning; and step 7: finding closest RESTful service and service function that are complementary to user input with SCRM, and implementing secondary recommendation.

2 . The secondary recommendation method based on the service complementary relation learning model for the RESTful service according to claim 1 , wherein in step 1.2, the service invocation data set comprises the following information:
1.2.1. a RESTful service: a RESTful application programming interface (API) service, which is represented by a symbol α; 1.2.2. a service invocation frequency: a total invocation frequency of a RESTful service, which is represented by a symbol Contain(α); 1.2.3. the service invocation sequence: a sequence consisting of invoked RESTful services, which is represented by a symbol c; 1.2.4. the service combination: a combination mode of RESTful services, which is represented by a symbol MA; and 1.2.5. a total service combination number: a total number of service combinations in the RESTful service invocation data set, which is represented by a symbol |MA|.

3 . The secondary recommendation method based on the service complementary relation learning model for the RESTful service according to claim 1 , wherein in step 1.3, the service complementary relation rule comprises the following information:
1.3.1. a support threshold: a lowest probability of simultaneous appearance of any two RESTful services α 1 and α 2 in c, which is represented by a symbol ω; 1.3.2. a confidence threshold: a lowest conditional probability of appearance of a RESTful service α 2 on the premise of appearance of a RESTful service α 1 in c, which is represented by a symbol δ; 1.3.3. co-occurrence: a condition that RESTful services α 1 and α 2 simultaneously appear in c, which is referred to as one time of co-occurrence, wherein a co-occurrence frequency is represented by a symbol Co(α 1 , α 2 ); and 1.3.4, the initial service complementary relation that exists between two RESTful services if a result computed on the basis of the service invocation frequency, the co-occurrence frequency and the total service combination number satisfies a given support threshold and a given confidence threshold.

4 . The secondary recommendation method based on the service complementary relation learning model for the RESTful service according to claim 1 , wherein in step 1.4, the initial service complementary relation is extracted as follows:
1.4.1. taking any two RESTful services from C, which are recorded as α 1 and α 2 respectively; 1.4.2. setting an invocation frequency Contain(α 1 ) of α i to be 0; 1.4.3. setting a co-occurre

Assignees

• Univ Jiliang China
• Zhejiang Zhejiang Tourism Investment Digital Tech Co Ltd
• Zhejiang Tourism Invest Group Co Ltd

Inventors

• Lu Jiawei
• Xiao Gang
• Cai Yun
• Zhang Xiaoye
• Xu Liangzhong
• Zheng Jiahong
• Wang Qibing
• Shi Ge
• Li Duanni

Classifications

• G06N3/08 Primary

  Learning methods · CPC title

• Y02D10/00

  Energy efficient computing, e.g. low power processors, power management or thermal management · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/042

  Knowledge-based neural networks; Logical representations of neural networks · CPC title

• G06F16/9535 Primary

  Search customisation based on user profiles and personalisation · CPC title