DINRec: Deep Interest Network Based API Recommendation Approach for Mashup Creation

Xiao, Yong; Liu, Jianxun; Hu, Rong; Cao, Buqing; Cao, Yingcheng

doi:10.1007/978-3-030-34223-4_12

Cited by 11 publications

(5 citation statements)

References 30 publications

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“…ISRec improves PaSRec with the measure of content similarity using word embedding. Moreover, it speeds up the search for neighbour mashups by clustering existing mashups offline. DINRec [31, 33]. This method applies a deep interest network (DIN) in CTR prediction to Web service recommendations.…”

Section: Experimental Setups and Results Analysismentioning

confidence: 99%

“…DINRec [31, 33]. This method applies a deep interest network (DIN) in CTR prediction to Web service recommendations.…”

Section: Experimental Setups and Results Analysismentioning

confidence: 99%

“…Inspired by the recent work on user interest representation in CTR prediction (e.g., Ref. [31,32]), DINRec [33] employs an attention mechanism to weigh selected services according to their relevance to the candidate service. Unfortunately, DIN-Rec is designed to develop mashups with some selected services.…”

Section: Multi-round Service Recommendationmentioning

confidence: 99%

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Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Geng

Wang

et al. 2022

CAAI Trans on Intel Tech

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Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases software developers' selection burden in developing new service-based systems such as mashups. Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service-oriented software engineering. Existing service recommendation approaches are mainly designed for mashup development in the single-round scenario. It is hard for them to effectively update recommendation results according to developers' requirements and behaviours (e.g. instant service selection). To address this issue, the authors propose a service bundle recommendation framework based on deep learning, DLISR, which aims to capture the interactions among the target mashup to build, selected (component) services, and the following service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending a candidate service. The authors also design two separate models for learning interactions from the perspectives of content and invocation history, respectively, and a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR can outperform several state-of-the-art service recommendation methods to develop new mashups iteratively.

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Section: Experimental Setups and Results Analysismentioning

confidence: 99%

“…DINRec [31, 33]. This method applies a deep interest network (DIN) in CTR prediction to Web service recommendations.…”

Section: Experimental Setups and Results Analysismentioning

confidence: 99%

See 1 more Smart Citation

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Geng

Wang

et al. 2022

CAAI Trans on Intel Tech

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“…Another framework known as random walks is based on the knowledge graph proposed by Kwapong and Fletcher (2019) for mashup-oriented API selection. Deep Interest Network-based API recommendation approach called DINRec is proposed by Xiao et al. (2019), for appropriate API selection.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, semantic similarity-based measurements are used by and Rahman et al (2017), to find the relationship between APIs (API description, popularity, tags and APIs usage history) and user required mashups. The most frequent similarity measurement technique adopted in literature is cosine similarity, in which we first concert documents into vectors form, then compute the similarity between two documents (Thung et al, 2017;Xu et al, 2018;Xiao et al, 2019;. Moreover, the hierarchical Dirichlet process (HDP) and Jensen-Shannon divergence-based technique are used by to find the similarity between the user query and APIs.…”

Section: A Study On Api Recommendationmentioning

confidence: 99%

A study on application programming interface recommendation: state-of-the-art techniques, challenges and future directions

Nawaz

Khan

Hussain

et al. 2022

LHT

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PurposeThis study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.Design/methodology/approachIn this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.FindingsThe outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.Research limitations/implicationsThis study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.Practical implicationsThis study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.Originality/valueAPI recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

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API-PROGRAM: An API Package Recommendation Model Based on the Graph Representation Learning Method

Cao

Liu

2021

Service-Oriented Computing

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DINRec: Deep Interest Network Based API Recommendation Approach for Mashup Creation

Cited by 11 publications

References 30 publications

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

A study on application programming interface recommendation: state-of-the-art techniques, challenges and future directions

API-PROGRAM: An API Package Recommendation Model Based on the Graph Representation Learning Method

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