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web api recommendation via Exploring Textual and Structural Semantics With Contrastive Learning and Joint Training

IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2025年第2期22卷 1558-1568页

作者： Kang, Guosheng Ren, Hongshuai Chen, Wanjun Liu, Jianxun Cao, Buqing Xu, Yu Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411199 Peoples R China Yunnan Univ Finance & Econ Yunnan Key Lab Serv Comp Kunming 650503 Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab Serv Comp & Novel Software Tech Xiangtan 411199 Peoples R China Univ Cambridge Dept Publ Hlth & Primary Care Cambridge Baker Syst Genom Initiat Cambridge CB2 1TN England Univ Cambridge British Heart Fdn Cardiovasc Epidemiol Unit Dept Publ Hlth & Primary Care Cambridge CB2 1TN England

With the advancement of service computing technology, software developers tend to consume a variety of web apis (Application Programming Interfaces, also named web services) from web api markets to create feature-rich Mashup applications to save time and cost. Under such a background, the ever-increasing number of web apis makes the service discovery become a challenge. Thus, web api recommendation becomes an effective means for service discovery. However, the existing approaches to web api recommendation still have limitations in extracting rich semantics sufficiently from functional description documents and service networks, resulting in a limited recommendation performance. To further improve the recommendation performance, this paper proposes an effective web api recommendation approach via exploring textual and structural semantics with contrastive learning and joint training, named CLJT. On one side, discriminative feature representations from textual and structural semantics could be derived by contrastive learning with information correlation across views. On the other side, the derived representations could be applicable to web api recommendation by joint training of the representation tasks and the recommendation task. Extensive experiments are conducted over a real-world dataset crawled from ***. The experimental results demonstrate the superiority of the proposed approach compared to the baseline methods.

关键词： Semantics Mashups Application programming interfaces Training Encoding Contrastive learning Bidirectional control Service computing Natural language processing Vectors Mashup creation web api recommendation textual semantic structural semantic contrastive learning joint training

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Harnessing the Power of Large Language Model for Effective web api recommendation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第7期21卷 5360-5370页

作者： Qin, Shaowei Zhao, Yiji Wu, Hao Zhang, Lei He, Qiang Yunnan Univ Sch Informat Sci & Engn Key Lab Intelligent Syst & Comp Yunnan Prov Kunming 650091 Peoples R China Nanjing Normal Univ Sch Elect & Automation Engn Nanjing 210024 Peoples R China Huazhong Univ Sci & Technol Natl Engn Res Ctr Big Data Technol & Syst Sch Comp Sci & Technol Serv Comp Technol & Syst LabCluster & Grid Comp L Wuhan 430074 Peoples R China

Various web api recommendation (AR) techniques have assisted developers in efficiently identifying suitable apis for mashup creation. With the emergence of large language models (LLMs), there has been increasing interest in leveraging LLMs for recommender systems. Although several approaches have attempted to utilize LLMs by framing recommendations as prompts, this approach is not ideally suited for AR due to fundamental differences in the training processes of LLMs and AR models. Consequently, it's crucial to conduct further research to identify effective applications of LLMs in AR. To this end, we propose a novel LLM-based generative solution for api recommendation (LLMAR) that combines instruction learning of multitask and multistage Low-Rank Adaptation fine-tuning based on LLaMA models. Experimental results on the Programmableweb dataset show that LLMAR significantly outperforms representative methods in regular and data-limited scenarios.

关键词： Mashups Adaptation models Training Data models Recommender systems Computational modeling Collaborative filtering Large language models Knowledge engineering Correlation Fine-tuning instruction learning large language model (LLM) mashup web api recommendation

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A LLM-driven and motif-informed linearizing graph transformer for web api recommendation

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Zheng, Xin Wang, Guiling Xu, Guiyue Yang, Jianye Han, Boyang Yu, Jian Beijing Key Lab Integrat & Anal Large scale Stream Beijing Peoples R China North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Auckland Univ Technol Dept Comp Sci Auckland New Zealand Southwest Jiaotong Univ Chengdu Peoples R China JD Intelligent Cities Res Xiongan Peoples R China

The rapid growth in the number of web apis makes it difficult for developers to find the appropriate ones. To tackle this issue, researchers have created various powerful automatic approaches for recommendations. Recently, a range of graph neural networks, drawing inspiration from Transformers, have incorporated global attention to enhance recommendations. However, these approaches still have limitations in terms of processing information between nodes and utilizing other valid information, which reduces their effectiveness in large-scale web api recommendations. To tackle these problems, this paper introduces a novel positional and semantic encoding method and motif-based linearizing graph Transformer for automatic web api recommendation. We integrate the semantic information of the nodes into the positional information by using a fine-tuned large language model and graph attention mechanism. Furthermore, we leverage motif information to alter the computational sequence of the Vanilla Transformer, achieving linear time complexity. Experimental results on the two real-world datasets demonstrate the suitability of our model for web api recommendation, surpassing existing state-of-the-art methods. In summary, our proposed technique exhibits promising results for web api recommendation and underscores the potential of global attention in this field.

关键词： Graph transformer web api recommendation Motif Higher-order connectivity Semantic information

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SEHGN: Semantic-Enhanced Heterogeneous Graph Network for web api recommendation

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2024年第5期17卷 2836-2849页

作者： Wang, Xuanye Xi, Meng Li, Ying Pan, Xiaohua Wu, Yangyang Deng, Shuiguang Yin, Jianwei Zhejiang Univ Sch Software Technol Hangzhou 310027 Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China Zhejiang Univ Binjiang Inst Hangzhou 310027 Peoples R China

With the growth of cloud computing, a large number of innovative mashup applications and web apis have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of web apis from multiple service ecosystems. Various web api recommendation methods have been proposed for Mashup creation, but most either treat different feature factor interactions equally or solely rely on requirements for api recommendation. These approaches face several challenges such as api compatibility dependencies, ambiguous definition and boundary dilemmas of apis, and sparse api invocation records. In this work, we propose a Semantic-Enhanced Heterogeneous Graph Network(SEHGN) for Mashup creation. To address the above deficiencies, we design a multi-semantic aggregator to capture semantic associations between features to encode multiple node-edge relationships. Then, we introduce a semantic embedding component to generate text embedding vectors for mashups and apis to learn global and local semantic information about text documents at different levels of abstraction. Finally, we fuse the output vectors to obtain a list of candidate web apis. Experiences are performed on real datasets, and statistical results show that SEHGN outperforms state-of-the-art models in terms of overall and long-tail web api recommendations.

关键词： Mashups Semantics Application programming interfaces Biological system modeling Vectors Ecosystems Computational modeling web api recommendation mashup creation heterogeneous network multi-semantic enhance semantic embedding

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apiRec: deep knowledge and diversity-aware web api recommendation

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2024年 1-13页

作者： Song, Fanfei Wang, Bin Xie, Xinqiang Pu, Rong Zhang, Qingbo Wang, Wei Northeastern Univ Sch Comp Sci & Engn Shenyang 110167 Liaoning Peoples R China Dalian Maritime Univ Coll Artificial Intelligence Dalian 116026 Liaoning Peoples R China Neusoft Cloud Technol Co LTD Dalian 116085 Liaoning Peoples R China

With the increasing number of web Services, providing accurate and efficient web apis to meet the growing demands of developers for Mashups has become a significant challenge. Most existing methods may have not explicitly considered the deep semantic relationships between web apis and Mashups, leading to poor recommendation accuracy. In addition to the accuracy of recommendation results, diversity is also considered a key factor in broadening user perspectives and enhancing user satisfaction. However, most current methods have failed to simultaneously explore the assistance of knowledge graphs in accuracy and diversity. To address this problem, this paper proposes a deep knowledge and diversity-awarded web api recommendation approach (named apiRec). Firstly, We utilize the Mashup-api collaborative invocation pattern and service category attributes to construct a streamlined knowledge graph. Secondly, in this hybrid Mashup-api structure, high-order relations between entities are explicitly implemented in the knowledge graph using an end-to-end approach to uncover hidden relationships among entities. Subsequently, attention mechanisms are employed to model and measure the importance of neighbors. Finally, to enhance the diversity of recommendation results, Maximal Marginal Relevance (MMR) is utilized to measure the accuracy and diversity of the recommended results simultaneously. To verify the effectiveness of apiRec, we conduct extensive experiments on real-world datasets collected from ***. The experimental results show that apiRec outperforms other state-of-the-art approaches by 2.62%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\textbf {2.62}}\%$$\end{document}, 4.20%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepac

关键词： Knowledge graph High-order relation Embedding propagation web api recommendation

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Motif-Based Linearizing Graph Transformer for web api recommendation 22nd

Motif-Based Linearizing Graph Transformer for Web API Recomm...

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22nd International Conference on Service Oriented Computing

作者： Zheng, Xin Wang, Guiling Zhang, Yuqi Han, Boyang Yu, Jian North China Univ Technol Sch Informat Sci & Technol Beijing Peoples R China Beijing Key Lab Integrat & Anal Large Scale Strea Beijing Peoples R China Auckland Univ Technol Dept Comp Sci Auckland New Zealand Southwest Jiaotong Univ Chengdu Peoples R China JD Intelligent Cities Res Beijing Peoples R China

ISBN: (纸本)9789819608072;9789819608089

The rapid growth of web services, often in the form of apis, has made it challenging for developers to choose the right ones. While graph neural network models have improved recommendations, they struggle with complex and long-distance node relationships, limiting their effectiveness in large-scale api recommendations. This paper introduces a motif-based linearizing graph Transformer with a novel positional encoding method, integrating global and local node relationships. By leveraging motif information, we achieve linear time complexity. Experiments show our model outperforms state-of-the-art methods, highlighting the potential of global attention in web api recommendations. The codes are available at https://***/xinzheng99/MotiFormer.

关键词： Graph Transformer web api recommendation Motif Higher-order connectivity Hierarchical positional encoding

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Biased Random Walk based web api recommendation in Heterogeneous Network

Biased Random Walk based Web API Recommendation in Heterogen...

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IEEE International Conference on web Services (IEEE ICWS)

作者： Hu, Xiaocao Dalian Univ Technol Sch Gen Educ Panjin Peoples R China

ISBN: (纸本)9798350368567;9798350368550

Benefit from the remarkable development of cloud computing, massive web apis have been published and numerous innovative mashups have emerged on the Internet. The rapid increase of available web apis brings a significant challenge on how to discover suitable apis for mashup creation. Various approaches have been proposed to recommend web apis, which mainly focus on employing collaborative filtering techniques such as matrix factorization and deep learning techniques such as graph neural network. However, existing studies suffer from a few limitations: collaborative filtering based methods are not able to capture the high-order connectivity information between apis and mashups, and deep learning based methods are not expressive enough to capture the diversity of connectivity patterns due to the simple graph structure. To tackle the limitations, we proposes a biased random walk based web api recommendation approach. We first construct a heterogeneous network to model different types of connectivity. And then we design a strategy in terms of neighbor nodes' types to guide the biased random walk and to capture diversity of high-order connectivity patterns. Finally we generate candidate web apis for mashups based on their embedding vectors. Experiments conducted on a real-world dataset show that our approach improves in precision by 13.89%, in recall by 17.16%, in F1 by 15.86%, and in NDCG by 2.85%.

关键词： web api recommendation Mashup Creation High-order Connectivity Biased Random Walk Heterogeneous Network

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Mashup-Oriented web api recommendation via Multi-Model Fusion and Multi-Task Learning

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2022年第6期15卷 3330-3343页

作者： Wu, Hao Duan, Yunhao Yue, Kun Zhang, Lei Yunnan Univ Sch Informat Sci & Engn Kunming 650091 Peoples R China Nanjing Normal Univ Sch Elect & Automation Engn Nanjing 210046 Peoples R China

As the number of web apis ever increases, choosing the appropriate apis for mashup creations becomes more difficult. To tackle this problem, various methods have been proposed to recommend apis to match requirements of mashups and achieved much success. However, there existed some challenges with feature fusion and utilization, textual requirement understanding, utilization of Mashup categories and compatibility evaluation. Therefore, we propose a neural framework (MTFM) based on multi-model fusion and multi-task learning for Mashup-oriented web api recommendation. MTFM exploits a semantic component to generate representations of requirements and introduces a feature interaction component to model the feature interaction between mashups and web apis. Output features of both components are further fused to predict the candidate apis, and this enables us to have both the advantages of content-based and collaborative filtering methods. We further introduce mashup category judgment as an auxiliary task, where both tasks are viewed as a multi-label learning problem and jointly optimized with multi-task learning. Also, we have extended MTFM to MTFM++ to take advantage of the metadata and quality features of apis, and proposed a metric for compatibility evaluation. Experimental results on the Programmableweb dataset show that our methods outperform most popular state-of-the-art methods.

关键词： Mashups Social networking (online) Task analysis Feature extraction Semantics Blogs Collaboration web api recommendation mashup multi-task learning multi-model fusion neural network

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Knowledge Graph Enhanced web api recommendation via Neighbor Information Propagation for Multi-service Application Development 18th

Knowledge Graph Enhanced Web API Recommendation via Neighbor...

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18th European-Alliance-for-Innovation (EAI) International Conference on Collaborative Computing - Networking, Applications and Worksharing (CollaborateCom)

作者： Chen, Zhen Li, Yujie Wang, Yuying Liu, Xiaowei Xing, Yifan Liu, Linlin You, Dianlong Shen, Limin Yanshan Univ Coll Informat Sci & Engn Qinhuangdao 066004 Peoples R China Chinese Acad Sci Natl Sci Libraries Beijing 100864 Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

In cloud era, web apis have been the best carrier for service delivery, capability replication and data output in multi-service application development. Currently, the number of web apis on the Internet is huge and growing exponentially. To enable accurate and fast web api selection for developers, researchers have proposed a variety of web api recommendation methods. However, existing methods cannot solve the inherent data sparsity problem well. In addition, existing methods use context information indirectly by finding neighbors or discretely through embedding techniques, while rich semantic information in the web api ecosystem is ignored. To solve the above problems, we firstly crawl and analyze web api data to construct a web api knowledge graph, which laid a data foundation for alleviating the data sparsity problem. Then, we propose a knowledge graph-enhanced web api recommendation model, so as to improve recommendation accuracy by capturing high-order structural information and semantic information. Typically, multivariate representations of user and web api are made by the neighbor information propagation in web api knowledge graph. The proposed model supports end-to-end learning for beneficial feature extraction. Finally, experiments results demonstrate the proposed model outperforms baselines significantly, thereby promoting the development of web api economy.

关键词： Multi-service application development web api recommendation Knowledge graph Neighbor information propagation

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PAREI: A progressive approach for web api recommendation by combining explicit and implicit information

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INFORMATION AND SOFTWARE TECHNOLOGY 2023年第1期162卷

作者： Wang, Ye Zhou, Aohui Huang, Qiao Wang, Xiaoyang Jiang, Bo Zhejiang Gongshang Univ Sch Comp Sci & Technol Hangzhou Peoples R China Univ New South Wales Sch Comp Sci & Engn Sydney Australia

Context: Mashup is an application with specific functions by combining web apis that can provide services or data on the Internet, thus avoiding the behavior of repeatedly building wheels. Recommending suitable web apis in the vast number of web apis on the Internet for Mashup developers has become a challenging problem. Previous studies often fail to fully exploit and effectively synthesize various types of information between web apis and ***: This work proposes a web api recommendation approach -PAREI by combining both explicit and implicit information to progressively optimize the recommendation ***: First, PAREI uses the explicit structural information between Mashups and web apis to construct the Call Relationship Network (CRN). Second, PAREI calculates explicit semantic similarities between developer's requirement and Mashups to obtain candidate Mashup nodes in CRN. Then PAREI further mines the implicit structural information between Mashups. A combined similarity score for each Mashup node is calculated. Finally, PAREI uses CRN to obtain candidate web apis related to candidate Mashup nodes, and integrates implicit semantic information of web apis with combined scores of corresponding Mashups, so as to obtain Top-K web ***: Comparison experiments show that PAREI has significantly improved the Recall, Precision, and MAP metrics compared with other approaches. Ablation experiments show that different types of information play various roles in web api recommendation, and different combination modes have different effects on the recommendation ***: This work constructs the PAREI model, which combines explicit and implicit information to obtain web api recommendation results through a progressive strategy. According to the experiment results, we believe that the PAREI approach can help Mashup developers to find demanded web apis rapidly and accurately.

关键词： web api recommendation Explicit information Implicit information Deep learning

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