Page 72 - 《软件学报》2025年第5期
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1972 软件学报 2025 年第 36 卷第 5 期
Expert Systems with Applications, 2020, 151: 113347. [doi: 10.1016/j.eswa.2020.113347]
[14] Yao LN, Wang XZ, Sheng QZ, Benatallah B, Huang CR. Mashup recommendation by regularizing matrix factorization with API
co-invocations. IEEE Trans. on Services Computing, 2021, 14(2): 502–515. [doi: 10.1109/TSC.2018.2803171]
[15] Tang B, Tang MD, Xia YM, Hsieh MY. Composition pattern-aware Web service recommendation based on depth factorisation machine.
Connection Science, 2021, 33(4): 870–890. [doi: 10.1080/09540091.2021.1911933]
[16] Cao BQ, Liu JX, Wen YP, Li HT, Xiao QX, Chen JJ. QoS-aware service recommendation based on relational topic model and
factorization machines for IoT Mashup applications. Journal of Parallel and Distributed Computing, 2019, 132: 177–189. [doi: 10.1016/j.
jpdc.2018.04.002]
[17] Nguyen M, Yu J, Nguyen T, Han YB. Attentional matrix factorization with context and co-invocation for service recommendation.
Expert Systems with Applications, 2021, 186: 115698. [doi: 10.1016/j.eswa.2021.115698]
[18] Kang GS, Liu JX, Xiao Y, Cao BQ, Xu Y, Cao ML. Neural and attentional factorization machine-based Web API recommendation for
Mashup development. IEEE Trans. on Network and Service Management, 2021, 18(4): 4183–4196. [doi: 10.1109/TNSM.2021.3125028]
[19] Xiao Y, Liu JX, Kang GS, Hu R, Cao BQ, Cao YC, Shi M. Structure reinforcing and attribute weakening network based API
recommendation approach for Mashup creation. In: Proc. of the 2020 IEEE Int’l Conf. on Web Services. Beijing: IEEE, 2020. 541–548.
[doi: 10.1109/ICWS49710.2020.00078]
[20] Liu MY, Zhu YQ, Xu HC, Tu ZY, Wang ZJ. T2L2: A tiny three linear layers model for service Mashup creation. In: Proc. of the 19th Int’l
Conf. on Service-oriented Computing. Berlin: Springer, 2021. 317–331. [doi: 10.1007/978-3-030-91431-8_20]
[21] Ma YT, Geng X, Wang J. A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Trans. on
Engineering Management, 2021, 68(1): 105–119. [doi: 10.1109/tem.2019.2961376]
[22] Yan RY, Fan YS, Zhang J, Zhang JQ, Lin HZ. Service recommendation for composition creation based on collaborative attention
convolutional network. In: Proc. of the 2021 IEEE Int’l Conf. on Web Services. Chicago: IEEE, 2021. 397–405. [doi: 10.1109/ICWS
53863.2021.00059]
[23] Cao BQ, Peng M, Qing YY, Liu JX, Kang GS, Li B, Fletcher KK. Web API recommendation via combining graph attention
representation and deep factorization machines quality prediction. Concurrency and Computation: Practice and Experience, 2022, 34(21):
e7069. [doi: 10.1002/cpe.7069]
[24] Huang DL, Tong XL, Yang HD. Web service recommendation based on graph attention network (GAT-WSR). In: Proc. of the 2022 Int’l
Conf. on Computer Communication and Informatics. Coimbatore: IEEE, 2022. 1–5. [doi: 10.1109/ICCCI54379.2022.9740941]
[25] Almarimi N, Ouni A, Bouktif S, Mkaouer MW, Kula RG, Saied MA. Web service API recommendation for automated Mashup creation
using multi-objective evolutionary search. Applied Soft Computing, 2019, 85: 105830. [doi: 10.1016/j.asoc.2019.105830]
[26] Gong WW, Zhang XY, Chen YF, He Q, Beheshti A, Xu XL, Yan C, Qi LY. DAWAR: Diversity-aware Web APIs recommendation for
Mashup creation based on correlation graph. In: Proc. of the 45th Int’l ACM SIGIR Conf. on Research and Development in Information
Retrieval. Madrid: ACM, 2022. 395–404. [doi: 10.1145/3477495.3531962]
[27] Campos R, Mangaravite V, Pasquali A, Jorge AM, Nunes C, Jatowt A. A text feature based automatic keyword extraction method for
single documents. In: Proc. of the 40th European on Conf. on Information Retrieval. Grenoble: Springer, 2018. 684–691. [doi: 10.1007/
978-3-319-76941-7_63]
[28] Liu JX, Shi M, Zhou D, Tang MD, Zhang TT. Topic model based tag recommendation method for Mashups. Chinese Journal of
Computers, 2017, 40(2): 520–534 (in Chinese with English abstract). [doi: 10.11897/SP.J.1016.2017.00520]
[29] Gao TY, Yao XC, Chen DQ. SimCSE: Simple contrastive learning of sentence embeddings. In: Proc. of the 2021 Conf. on Empirical
Methods in Natural Language Processing. Punta Cana: ACL, 2021. 6894–6910. [doi: 10.18653/v1/2021.emnlp-main.552]
[30] Cen YK, Zou X, Zhang JW, Yang HX, Zhou JR, Tang J. Representation learning for attributed multiplex heterogeneous network. In:
Proc. of the 25th ACM SIGKDD Int’l Conf. on Knowledge Discovery & Data Mining. Anchorage: ACM, 2019. 1358–1368. [doi: 10.
1145/3292500.3330964]
[31] Dong YX, Chawla NV, Swami A. Metapath2vec: Scalable representation learning for heterogeneous networks. In: Proc. of the 23rd ACM
SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Halifax: ACM, 2017. 135–144. [doi: 10.1145/3097983.3098036]
[32] Jiang R, Han SS, Yu YM, Ding WP. An access control model for medical big data based on clustering and risk. Information Sciences,
2023, 621: 691–707. [doi: 10.1016/j.ins.2022.11.102]
[33] Wu SQ, Shen SG, Xu XL, Chen Y, Zhou XK, Liu DN, Xue X, Qi LY. Popularity-aware and diverse Web APIs recommendation based on
correlation graph. IEEE Trans. on Computational Social Systems, 2023, 10(2): 771–782. [doi: 10.1109/TCSS.2022.3168595]
[34] Zhao Y, Qiao Y, He KQ. A novel tagging augmented LDA model for clustering. Int’l Journal of Web Services Research, 2019, 16(3):
59–77. [doi: 10.4018/IJWSR.2019070104]