功能互补关系增强的云API推荐方法

doi:10.11959/j.issn.1000-436x.2023093

Abstract

Abstract:

Current cloud application program interface (API) recommendation methods mainly use similarity calculation or historical calls of Mashup to generate recommendation results, while ignoring the beneficial functional complementarity (FC) between Mashup and cloud API.To address the above issue, a FC relationship enhanced cloud API recommendation approach was proposed.Firstly, label co-occurrence was applied to describe the FC relationship.Then, the FC score was calculated to describe the degree of FC between the cloud API and the Mashup, and the FC vector was learned to describe the potential FC relationship.Based on this, FC scores and FC vectors were embedded into the cloud API recommendation model, so that FC relationship played a key role in the cloud API recommendation process.Experiments were conducted on real-world cloud API datasets, and the AUC, F1 and HR@5 of the proposed approach improved by an average of 2.32%, 1.86% and 9.15%, respectively, in the sparse scenario.Finally, the proposed approach can improve the accuracy of cloud API recommendation results, while improving the recommendation performance of long-tail cloud API.

Key words: cloud API recommendation, functional complementarity, label co-occurrence, long tail cloud API

CLC Number:

TP393

Zhen CHEN, Wenhui CHEN, Xiaowei LIU, Dianlong YOU, Linlin LIU, Limin SHEN. Functional complementarity relationship enhanced cloud API recommendation method[J]. Journal on Communications, 2023, 44(6): 125-137.

Figures/Tables 11

References 30

[1]	PAPAZOGLOU M P , HEUVEL W J V D . Service oriented architectures:approaches,technologies and research issues[J]. The VLDB Journal, 2007,16(3): 389-415.
[2]	YU J , BENATALLAH B , CASATI F ,et al. Understanding mashup development[J]. IEEE Internet Computing, 2008,12(5): 44-52.
[3]	陈真, 乞文超, 贺鹏飞 ,等. 云应用程序编程接口安全研究综述:威胁与防护[J]. 电子与信息学报, 2023,45(1): 371-382.
	CHEN Z , QI W C , HE P F ,et al. A survey for cloud application programming interface security:threats and protection[J. Journal of Electronics ＆ Information Technology, 2023,45(1): 371-382.
[4]	WU H , DUAN Y H , YUE K ,et al. Mashup-oriented Web API recommendation via multi-model fusion and multi-task learning[J]. IEEE Transactions on Services Computing, 2022,15(6): 3330-3343.
[5]	CAO B , PENG M , QING Y ,et al. Web API recommendation via combining graph attention representation and deep factorization machines quality prediction[J]. Concurrency and Computation:Practice and Experience, 2022,34(21): E7069.
[6]	ZHANG M Y , BOCKSTEDT J . Complements and substitutes in online product recommendations:the differential effects on consumers’ willingness to pay[J]. Information ＆ Management, 2020:doi.org/10.1016/j.im.2020.103341.
[7]	WANG R , WANG J , SU Z . Learning compatibility knowledge for outfit recommendation with complementary clothing matching[J]. Computer Communications, 2022,181: 320-328.
[8]	ZHANG W , CHEN Z Y , ZHA H Y ,et al. Learning from substitutable and complementary relations for graph-based sequential product recommendation[J]. ACM Transactions on Information Systems, 2022,40(2): 1-28.
[9]	WANG Z H , JIANG Z H , REN Z C ,et al. A path-constrained framework for discriminating substitutable and complementary products in E-commerce[C]// Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. New York:ACM Press, 2018: 619-627.
[10]	MCAULEY J , PANDEY R , LESKOVEC J . Inferring networks of substitutable and complementary products[C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM Press, 2015: 785-794.
[11]	HUYNH C P , CIPTADI A , TYAGI A ,et al. CRAFT:complementary recommendation by adversarial feature transform[C]// European Conference on Computer Vision. Berlin:Springer, 2019: 54-66.
[12]	BOTANGEN K A , YU J , SHENG Q Z ,et al. Geographic-aware col-laborative filtering for Web service recommendation[J]. Expert Sys-tems With Applications, 2020:doi.org/10.1016/j.eswa.2020.113347.
[13]	XIONG R , WANG J , ZHANG N ,et al. Deep hybrid collaborative filtering for Web service recommendation[J]. Expert Systems with Applications, 2018,110: 191-205.
[14]	YAO L N , WANG X Z , SHENG Q Z ,et al. Mashup recommendation by regularizing matrix factorization with API co-invocations[J]. IEEE Transactions on Services Computing, 2021,14(2): 502-515.
[15]	TIAN G , WANG J , HE K Q ,et al. Integrating implicit feedbacks for time-aware Web service recommendations[J]. Information Systems Frontiers, 2017,19(1): 75-89.
[16]	NIU H , KEIVANLOO I , ZOU Y . API usage pattern recommendation for software development[J]. Journal of Systems and Software, 2017,129: 127-139.
[17]	ALMARIMI N , OUNI A , BOUKTIF S ,et al. Web service API recommendation for automated mashup creation using multi-objective evolutionary search[J]. Applied Soft Computing, 2019,85:105830.
[18]	NA?M H , AZNAG M , DURAND N ,et al. Semantic pattern mining based Web service recommendation[C]// Proceedings of International Conference on Service-Oriented Computing. Berlin:Springer, 2016: 417-432.
[19]	DOJCHINOVSKI M , KUCHAR J , VITVAR T ,et al. Personalised graph-based selection of Web APIs[C]// Proceedings of International Semantic Web Conference. Berlin:Springer, 2012: 34-48.
[20]	RICHARDSON M , DOMINOWSKA E , RAGNO R . Predicting clicks:estimating the click-through rate for new ads[C]// Proceedings of the 16th International Conference on World Wide Web. New York:ACM Press, 2007: 521-530.
[21]	RENDLE S . Factorization machines[C]// Proceedings of IEEE International Conference on Data Mining. Piscataway:IEEE Press, 2011: 995-1000.
[22]	YU Y T , WANG Z , YUAN B . An input-aware factorization machine for sparse prediction[C]// Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. California:International Joint Conferences on Artificial Intelligence Organization, 2019: 1466-1472.
[23]	LU W T , YU Y T , CHANG Y Z ,et al. A dual input-aware factorization machine for CTR prediction[C]// Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. California:International Joint Conferences on Artificial Intelligence Organization, 2020: 3139-3145.
[24]	HE X N , CHUA T S . Neural factorization machines for sparse predictive analytics[C]// Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2017: 355-364.
[25]	XIAO J , YE H , HE X ,et al. Attentional factorization machines:learning the weight of feature interactions via attention networks[J]. arXiv Preprint,arXiv:1708.04617, 2017.
[26]	CHENG H T , KOC L , HARMSEN J ,et al. Wide ＆ deep learning for recommender systems[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM Press, 2016: 7-10.
[27]	WANG R X , FU B , FU G ,et al. Deep ＆ cross network for ad click predictions[C]// Proceedings of the ADKDD’17. New York:ACM Press, 2017: 1-7.
[28]	GUO H F , TANG R M , YE Y M ,et al. DeepFM:a factorization-machine based neural network for CTR prediction[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. New York:ACM Press, 2017: 1725-1731.
[29]	LIAN J X , ZHOU X H , ZHANG F Z ,et al. xDeepFM:combiningexplicit and implicit feature interactions for recommender systems[C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining. New York:ACM Press, 2018: 1754-1763.
[30]	SONG W , SHI C , XIAO Z ,et al. Autoint:automatic feature interaction learning via self-attentive neural networks[C]// Proceedings of the 28th ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2019: 1161-1170.

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云API	云API标签	Mashup标签	Mashup占比
PayPal	支付	电商	37.14%
Last.FM	播客	音乐	74.03%
Twitter	博客	社交	51.63%
YouTube	视频	媒体	71.23%

模型	AUC				F1				HR@5
模型	Base	+CS	+CV	+CSCV	Base	+CS	+CV	+CSCV	Base	+CS	+CV	+CSCV
LR	0.892	0.897	0.899	0.905	0.774	0.777	0.777	0.785	0.379	0.392	0.393	0.408
FM	0.893	0.895	0.902	0.907	0.778	0.785	0.781	0.789	0.393	0.407	0.408	0.425
IFM	0.895	0.897	0.904	0.911	0.787	0.799	0.797	0.799	0.404	0.419	0.420	0.438
DIFM	0.897	0.899	0.904	0.912	0.795	0.804	0.802	0.807	0.408	0.424	0.424	0.443
NFM	0.898	0.901	0.905	0.913	0.799	0.808	0.807	0.812	0.426	0.445	0.445	0.464
AFM	0.899	0.903	0.905	0.924	0.807	0.817	0.816	0.820	0.437	0.458	0.458	0.478
WDL	0.894	0.901	0.901	0.918	0.809	0.823	0.820	0.825	0.444	0.467	0.468	0.486
DCN	0.896	0.901	0.902	0.918	0.820	0.834	0.825	0.837	0.449	0.473	0.473	0.492
DeepFM	0.898	0.905	0.905	0.925	0.826	0.838	0.837	0.843	0.457	0.482	0.483	0.502
xDeepFM	0.899	0.906	0.906	0.928	0.830	0.844	0.843	0.852	0.472	0.500	0.501	0.520
AutoInt	0.902	0.907	0.907	0.931	0.840	0.850	0.850	0.862	0.487	0.519	0.520	0.538
平均值	0.897	0.901	0.904	0.918	0.806	0.816	0.814	0.821	0.432	0.453	0.454	0.472

模型	AUC				F1				HR@5
模型	Base	+CS	+CV	+CSCV	Base	+CS	+CV	+CSCV	Base	+CS	+CV	+CSCV
LR	0.616	0.666	0.660	0.707	0.685	0.724	0.718	0.774	0.278	0.307	0.308	0.327
FM	0.619	0.667	0.663	0.709	0.688	0.726	0.721	0.791	0.288	0.319	0.319	0.339
IFM	0.622	0.669	0.663	0.710	0.694	0.728	0.727	0.795	0.296	0.329	0.330	0.352
DIFM	0.628	0.670	0.665	0.713	0.700	0.736	0.735	0.797	0.313	0.350	0.350	0.372
NFM	0.629	0.673	0.669	0.715	0.714	0.741	0.739	0.799	0.320	0.357	0.357	0.381
AFM	0.632	0.674	0.671	0.717	0.716	0.743	0.744	0.807	0.325	0.365	0.364	0.389
WDL	0.630	0.671	0.665	0.714	0.722	0.758	0.759	0.809	0.323	0.364	0.364	0.389
DCN	0.632	0.673	0.669	0.716	0.724	0.764	0.763	0.820	0.340	0.384	0.384	0.410
DeepFM	0.635	0.674	0.672	0.719	0.726	0.769	0.778	0.825	0.353	0.398	0.399	0.427
xDeepFM	0.639	0.679	0.675	0.723	0.728	0.774	0.785	0.837	0.354	0.401	0.402	0.431
AutoInt	0.642	0.681	0.680	0.728	0.732	0.789	0.787	0.840	0.369	0.419	0.419	0.450
平均值	0.629	0.672	0.669	0.716	0.712	0.750	0.751	0.809	0.324	0.363	0.363	0.388

Functional complementarity relationship enhanced cloud API recommendation method

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