Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Jingyun Zhang; Wenjie Zhu; Byoung Jin Ahn; Yongsheng Zhou

doi:10.26599/TST.2023.9010012

Tsinghua Science and Technology 2024, 29(1): 174-184 https://doi.org/10.26599/TST.2023.9010012

Open Access | Issue | Published: 21 August 2023

Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Show Author's Information Hide Author's Information Jingyun Zhang^{¹^,²}, Wenjie Zhu^{³^,⁴}, Byoung Jin Ahn^¹(

), Yongsheng Zhou^{¹^,⁵}

1School of Design, Dongseo University, Busan 47011, Republic of Korea

2Institute of Art and Design, Jiangsu University of Technology, Changzhou 213001, China

3Jinling Wenyun Art Design Co., Ltd., Zhenjiang 212000, China

4Institute of Art and Design, Krirk University, Bangkok 10220, Thailand

5Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China

Keywords:

E-commerce, composite recommendation, artwork, user keywords, correlation graph search

Cite this article:

Zhang J, Zhu W, Ahn BJ, et al. Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search. Tsinghua Science and Technology, 2024, 29(1): 174-184. https://doi.org/10.26599/TST.2023.9010012

Download citation

EndNote(RIS)

BibTeX

222

Views

Downloads

Citations

Crossref

WoS

Scopus

CSCD

Abstract Full text About this article

Abstract

With the ever-increasing diversification of people’s interests and preferences, artwork has become one of the most popular commodities or investment goods in E-commerce, and it increasingly attracts the attention of the public. Currently, many real-world or virtual artworks can be found in E-commerce, and finding a means to recommend them to appropriate users has become a significant task to alleviate the heavy burden on artwork selection decisions by users. Existing research mainly studies the problem of single-artwork recommendation while neglecting the more practical but more complex composite recommendation of artworks in E-commerce, which considerably influences the quality of experience of potential users, especially when they need to select a set of artworks instead of a single artwork. Inspired by this limitation, we put forward a novel composite recommendation approach to artworks by a user keyword-driven correlation graph search named ${ART}_{com-rec}$ . Through ${ART}_{com-rec}$ , the recommender system can output a set of artworks (e.g., an artwork composite solution) in E-commerce by considering the keywords typed by a user to indicate his or her personalized preferences. Finally, we validate the feasibility of the ${ART}_{com-rec}$ approach by a set of simulated experiments on a real-world PW dataset.

Full text

Abstract

Full text

Outline

About this article

Composite Recommendation of Artworks in E-Commerce Based on User Keyword-Driven Correlation Graph Search

Show Author's information Hide Author's Information Jingyun Zhang^{¹^,²}, Wenjie Zhu^{³^,⁴}, Byoung Jin Ahn^¹(

), Yongsheng Zhou^{¹^,⁵}

1School of Design, Dongseo University, Busan 47011, Republic of Korea

2Institute of Art and Design, Jiangsu University of Technology, Changzhou 213001, China

3Jinling Wenyun Art Design Co., Ltd., Zhenjiang 212000, China

4Institute of Art and Design, Krirk University, Bangkok 10220, Thailand

5Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Shouguang 262700, China

Abstract

Keywords: E-commerce, composite recommendation, artwork, user keywords, correlation graph search

References(48)

[1]

C. Peng, C. Zhang, X. Xue, J. Gao, H. Liang, and Z. Niu, Cross-modal complementary network with hierarchical fusion for multimodal sentiment classification, Tsinghua Science and Technology, vol. 27, no. 4, pp. 664–679, 2021.

DOI Google Scholar

[2]

L. Kong, L. Wang, W. Gong, C. Yan, Y. Duan, and L. Qi, LSH-aware multitype health data prediction with privacy preservation in edge environment, World Wide Web, vol. 25, pp. 1793–1808, 2021.

DOI Google Scholar

[3]

A. Lieto, G. L. Pozzato, M. Striani, S. Zoia, and R. Damiano, DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion, Cogn. Syst. Res., vol. 77, pp. 1–17, 2023.

DOI Google Scholar

[4]

D. Fenner, Art education and the investment of attention, J. Aesthetic Educ., vol. 56, no. 2, pp. 23–36, 2022.

DOI Google Scholar

[5]

Z. Wei, P. Paliyawan, and R. Thawonmas, A card game for collecting human-perceived similarity data of artwork images, IEEE Access, vol. 10, pp. 8103–8111, 2022.

DOI Google Scholar

[6]

Q. Cao, W. Zhang, and Y. Zhu, Deep learning-based classification of the polar emotions of “moe”-style cartoon pictures, Tsinghua Science and Technology, vol. 26, no. 3, pp. 275–286, 2020.

DOI Google Scholar

[7]

L. Qi, W. Lin, X. Zhang, W. Dou, X. Xu, and J. Chen, A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development, IEEE Trans. Knowl. Data Eng., vol. 35, no. 6, pp. 5444–5457, 2022.

DOI Google Scholar

[8]

S. Wu, S. Shen, X. Xu, Y. Chen, X. Zhou, D. Liu, X. Xue, and L. Qi, Popularity-aware and diverse web APIs recommendation based on correlation graph, IEEE Trans. Comput. Soc. Syst., vol. 10, no. 2, pp. 771–782, 2022.

DOI Google Scholar

[9]

S. Meng, Z. Gao, Q. Li, H. Wang, H. N. Dai, and L. Qi, Security-driven hybrid collaborative recommendation method for cloud-based iot services, Comput. Secur., vol. 97, p. 101950, 2020.

DOI Google Scholar

[10]

Y. Jin, W. Guo, and Y. Zhang, A time-aware dynamic service quality prediction approach for services, Tsinghua Science and Technology, vol. 25, no. 2, pp. 227–238, 2019.

DOI Google Scholar

[11]

P. Messina, V. Dominguez, D. Parra, C. Trattner, and A. Soto, Content-based artwork recommendation: Integrating painting metadata with neural and manually-engineered visual features, User Modeling User Adapt. Interact., vol. 29, no. 2, pp. 251–290, 2019.

DOI Google Scholar

[12]

V. Dominguez, P. Messina, D. Parra, D. Mery, C. Trattner, and A. Soto, Comparing neural and attractiveness-based visual features for artwork recommendation, in Proc. 2^nd Workshop on Deep Learning for Recommender Systems, Como, Italy, 2017, pp. 55–59.

DOI Google Scholar

[13]

R. Gu, Y. Chen, S. Liu, H. Dai, G. Chen, K. Zhang, Y. Che, and Y. Huang, Liquid: Intelligent resource estimation and network-efficient scheduling for deep learning jobs on distributed GPU clusters, IEEE Trans. Parallel Distributed Syst., vol. 33, no. 11, pp. 2808–2820, 2021.

DOI Google Scholar

[14]

R. Gu, K. Zhang, Z. Xu, Y. Che, B. Fan, H. Hou, H. Dai, L. Yi, Y. Ding, G. Chen, et al., Fluid: Dataset abstraction and elastic acceleration for cloud-native deep learning training jobs, in Proc. 2022 IEEE 38^th Int. Conf. Data Engineering (ICDE), Kuala Lumpur, Malaysia, 2022, pp. 2182–2195.

DOI Google Scholar

[15]

M. Han and H. J. Ahn, Testing the application of the personalized recommendation method for artworks, Mediterr. J. Soc. Sci., vol. 6, no. 6 S2, p. 433, 2015.

DOI Google Scholar

[16]

I. Benouaret and D. Lenne, Personalizing the museum experience through context-aware recommendations, in Proc. 2015 IEEE Int. Conf. Systems, Man, and Cybernetics, Hong Kong, China, 2015, pp. 743–748.

DOI Google Scholar

[17]

Y. Wang, S. Wang, N. Stash, L. Aroyo, and G. Schreiber, Enhancing content-based recommendation with the task model of classification, in Proc. 17^th Int. Conf. on Knowledge Engineering and Knowledge Management, Lisbon, Portugal, 2010, pp. 431–440.

DOI Google Scholar

[18]

K. Shen, R. Tu, R. Yao, S. Wang, and A. K. Luhach, Decorative art pattern mining and discovery based on group user intelligence, J. Organ. End User Comput., vol. 33, no. 6, pp. 1–12, 2021.

DOI Google Scholar

[19]

Y. Wang, N. Stash, R. Sambeek, Y. Schuurmans, L. Aroyo, G. Schreiber, and P. Gorgels, Cultivating personalized museum tours online and on-site, Interdiscip. Sci. Rev., vol. 34, nos. 2&3, pp. 139–153, 2009.

DOI Google Scholar

[20]

R. Gu, Y. Zhou, Z. Wang, C. Yuan, and Y. Huang, Penguin: Efficient query-based framework for replaying large scale historical data, IEEE Trans. Parallel Distributed Syst., vol. 29, no. 10, pp. 2333–2345, 2018.

DOI Google Scholar

[21]

I. Gatti, A hybrid approach for artwork recommendation, in Proc. 2019 IEEE Second Int. Conf. Artificial Intelligence and Knowledge Engineering (AIKE), Sardinia, Italy, 2019, pp. 281–284.

DOI Google Scholar

[22]

S. Rossi, F. Barile, C. Galdi, and L. Russo, Artworks sequences recommendations for groups in museums, in Proc. 2016 12^th Int. Conf. Signal-Image Technology & Internet-Based Systems (SITIS), Naples, Italy, 2016, pp. 455–462.

DOI Google Scholar

[23]

H. Gong, Text mining of movie animation user comments and video artwork recommendation based on machine learning, Secur. Commun. Netw., vol. 2022, p. 2800481, 2022.

DOI Google Scholar

[24]

L. Qi, Q. He, F. Chen, X. Zhang, W. Dou, and Q. Ni, Data-driven web APIs recommendation for building web applications, IEEE Trans. Big Data, vol. 8, no. 3, pp. 685–698, 2020.

DOI Google Scholar

[25]

W. Gao and J. Wu, A novel framework for service set recommendation in mashup creation, in Proc. 2017 IEEE Int. Conf. Web Services (ICWS), Honolulu, HI, USA, 2017, pp. 65–72.

DOI Google Scholar

[26]

Q. Gu, J. Cao, and Q. Peng, Service package recommendation for mashup creation via mashup textual description mining, in Proc. 2016 IEEE Int. Conf. Web Services (ICWS), San Francisco, CA, USA, 2016, pp. 452–459.

DOI Google Scholar

[27]

H. Dai, J. Yu, M. Li, W. Wang, A. X. Liu, J. Ma, L. Qi, and G. Chen, Bloom filter with noisy coding framework for multi-set membership testing, IEEE Trans. Knowl. Data Eng., vol. 35, no. 7, pp. 6710–6724, 2022.

DOI Google Scholar

[28]

L. Kong, G. Li, W. Rafique, S. Shen, Q. He, M. R. Khosravi, R. Wang, and L. Qi, Time-aware missing healthcare data prediction based on ARIMA model, IEEE/ACM Trans. Comput. Biol. Bioinform., .

DOI Google Scholar

[29]

X. Zhou, W. Liang, K. Yan, W. Li, K. I. K. Wang, J. Ma, and Q. Jin, Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything, IEEE Internet Things J., vol. 10, no. 4, pp. 3295–3304, 2022.

DOI Google Scholar

[30]

Y. Li, S. Xia, M. Zheng, B. Cao, and Q. Liu, Lyapunov optimization-based trade-off policy for mobile cloud offloading in heterogeneous wireless networks, IEEE Trans. Cloud Comput., vol. 10, no. 1, pp. 491–505, 2019.

DOI Google Scholar

[31]

X. Zhou, W. Liang, K. I. K. Wang, and L. T. Yang, Deep correlation mining based on hierarchical hybrid networks for heterogeneous big data recommendations, IEEE Trans. Comput. Soc. Syst., vol. 8, no. 1, pp. 171–178, 2020.

DOI Google Scholar

[32]

F. Wang, G. Li, Y. Wang, W. Rafique, M. R. Khosravi, G. Liu, Y. Liu, and L. Qi, Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city, ACM Trans. Internet Technol., .

DOI Google Scholar

[33]

X. Zhou, X. Yang, J. Ma, and K. I. K. Wang, Energy-efficient smart routing based on link correlation mining for wireless edge computing in IoT, IEEE Internet Things J., vol. 9, no. 16, pp. 14988–14997, 2021.

DOI Google Scholar

[34]

X. Xue, S. Wang, L. Zhang, Z. Feng, and Y. Guo, Social learning evolution (SLE): Computational experiment-based modeling framework of social manufacturing, IEEE Trans. Ind. Inform., vol. 15, no. 6, pp. 3343–3355, 2019.

DOI Google Scholar

[35]

Q. Luo, D. Yu, Z. Cai, X. Lin, G. Wang, and X. Cheng, Toward maintenance of hypercores in large-scale dynamic hypergraphs, VLDB J., vol. 32, no. 3, pp. 647–664, 2023.

DOI Google Scholar

[36]

Y. Li, J. Liu, B. Cao, and C. Wang, Joint optimization of radio and virtual machine resources with uncertain user demands in mobile cloud computing, IEEE Trans. Multimed., vol. 20, no. 9, pp. 2427–2438, 2018.

DOI Google Scholar

[37]

D. Zhou, X. Xue, and Z. Zhou, SLE2: The improved social learning evolution model of cloud manufacturing service ecosystem, IEEE Trans. Ind. Inform., vol. 18, no. 12, pp. 9017–9026, 2022.

DOI Google Scholar

[38]

Q. Luo, D. Yu, X. Cheng, H. Sheng, and W. Lyu, Exploring truss maintenance in fully dynamic graphs: A mixed structure-based approach, IEEE Trans. Comput., vol. 72, no. 3, pp. 707–718, 2022.

DOI Google Scholar

[39]

Y. Li, C. Liao, Y. Wang, and C. Wang, Energy-efficient optimal relay selection in cooperative cellular networks based on double auction, IEEE Trans. Wirel. Commun., vol. 14, no. 8, pp. 4093–4104, 2015.

DOI Google Scholar

[40]

X. Xue, G. Li, D. Zhou, Y. Zhang, L. Zhang, Y. Zhao, Z. Feng, L. Cui, Z. Zhou, X. Sun, et al., Research roadmap of service ecosystems: A crowd intelligence perspective, International Journal of Crowd Science, vol. 6, no. 4, pp. 195–222, 2022.

DOI Google Scholar

[41]

D. Yu, D. Wang, Q. Luo, Y. Zheng, G. Wang, and Z. Cai, Stable structural clustering in uncertain graphs, Inf. Sci., vol. 586, pp. 596–610, 2022.

DOI Google Scholar

[42]

W. Liang, Y. Hu, X. Zhou, Y. Pan, and K. I. K. Wang, Variational few-shot learning for microservice-oriented intrusion detection in distributed industrial IoT, IEEE Trans. Ind. Inform., vol. 18, no. 8, pp. 5087–5095, 2021.

DOI Google Scholar

[43]

W. Wang, Y. Wang, P. Duan, T. Liu, X. Tong, and Z. Cai, A triple real-time trajectory privacy protection mechanism based on edge computing and blockchain in mobile crowdsourcing, IEEE Transactions on Mobile Computing, .

DOI Google Scholar

[44]

Q. Zhang, Y. Wang, G. Yin, X. Tong, A. M. V. V. Sai, and Z. Cai, Two-stage bilateral online priority assignment in spatio-temporal crowdsourcing, IEEE Trans. Serv. Comput., vol. 16, no. 3, pp. 2267–2282, 2022.

DOI Google Scholar

[45]

X. Zhou, W. Liang, K. I. K. Wang, R. Huang, and Q. Jin, Academic influence aware and multidimensional network analysis for research collaboration navigation based on scholarly big data, IEEE Trans. Emerg. Top. Comput., vol. 9, no. 1, pp. 246–257, 2018.

DOI Google Scholar

[46]

D. Yu, N. Wang, Q. Luo, F. Li, J. Yu, X. Cheng, and Z. Cai, Fast core maintenance in dynamic graphs, IEEE Trans. Comput. Soc. Syst., vol. 9, no. 3, pp. 710–723, 2021.

DOI Google Scholar

[47]

M. Xu, C. Wang, Y. Zou, D. Yu, X. Cheng, and W. Lyu, Curb: Trusted and scalable software-defined network control plane for edge computing, in Proc. 2022 IEEE 42^nd Int. Conf. Distributed Computing Systems (ICDCS), Bologna, Italy, 2022, pp. 492–502.

DOI Google Scholar

[48]

X. Zhou, W. Liang, W. Li, K. Yan, S. Shimizu, and K. I. K. Wang, Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system, IEEE Internet Things J., vol. 9, no. 12, pp. 9310–9319, 2021.

DOI Google Scholar

About this article

Publication history

Rights and permissions

Publication history

Received: 20 January 2023

Revised: 15 February 2023

Accepted: 24 February 2023

Published: 21 August 2023

Issue date: February 2024

Copyright

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).