AI Chat Paper
Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
{{expandStatus?'Exit ':''}}Advanced Search
As living standards improve, the demand for artworks has been escalating, transcending beyond the realm of mere basic human necessities. However, amidst an extensive array of artwork choices, users often struggle to swiftly and accurately identify their preferred piece. In such scenarios, a recommendation system can be invaluable, assisting users in promptly pinpointing the desired artworks for better service design. Despite the escalating demand for artwork recommendation systems, current research fails to adequately meet these needs. Predominantly, existing artwork recommendation methodologies tend to disregard users’ implicit interests, thereby overestimating their capability to articulate their preferences in full and often neglecting the nuances of their diverse interests. In response to these challenges, we have developed a weighted artwork correlation graph and put forth an embedding-based keyword-driven artwork search and recommendation methodology. Our approach transforms the keywords that delineate user interests into word embedding vectors. This allows for an effective distinction between the user’s core and peripheral interests. Subsequently, we employ a dynamic programming algorithm to extract artworks from the correlation graph, thereby obtaining artworks that align with the user’s explicit keywords and implicit interests. We have conducted an array of experiments using real-world datasets to validate our approach. The results attest to the superiority of our method in terms of its efficacy in searching and recommending artworks.
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, 2022.
Q. Hua, L. Chen, P. Li, S. Zhao, and Y. Li, A pixel–channel hybrid attention model for image processing, Tsinghua Science and Technology, vol. 27, no. 5, pp. 804–816, 2022.
Y. Yan, B. Lu, and T. Xu, Exploring the relationship between host self-description and consumer purchase behavior using a self-presentation strategy, Systems, vol. 11, no. 8, p. 430, 2023.
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 Model. User Adapt. Interact., vol. 29, no. 2, pp. 251–290, 2019.
D. Yan, Y. Zhao, Z. Yang, Y. Jin, and Y. Zhang, FedCDR: Privacy-preserving federated cross-domain recommendation, Digit. Commun. Netw., vol. 8, no. 4, pp. 552–560, 2022.
M. Hong, J. J. Jung, F. Piccialli, and A. Chianese, Social recommendation service for cultural heritage, Pers. Ubiquitous Comput., vol. 21, no. 2, pp. 191–201, 2017.
J. Zhang, Y. Wang, Z. Yuan, and Q. Jin, Personalized real-time movie recommendation system: Practical prototype and evaluation, Tsinghua Science and Technology, vol. 25, no. 2, pp. 180–191, 2020.
H. Ko, S. Lee, Y. Park, and A. Choi, A survey of recommendation systems: Recommendation models, techniques, and application fields, Electronics, vol. 11, no. 1, p. 141, 2022.
S. Ahmadian, M. Ahmadian, and M. Jalili, A deep learning based trust- and tag-aware recommender system, Neurocomputing, vol. 488, pp. 557–571, 2022.
F. Fkih, Similarity measures for collaborative filtering-based recommender systems: Review and experimental comparison, J. King Saud Univ. Comput. Inf. Sci., vol. 34, no. 9, pp. 7645–7669, 2022.
Y. Miao, X. Bai, Y. Cao, Y. Liu, F. Dai, F. Wang, L. Qi, and W. Dou, A novel short-term traffic prediction model based on SVD and ARIMA with blockchain in industrial Internet of Things, IEEE Internet Things J., vol. 10, no. 24, pp. 21217–21226, 2023.
H. Papadakis, A. Papagrigoriou, C. Panagiotakis, E. Kosmas, and P. Fragopoulou, Collaborative filtering recommender systems taxonomy, Knowl. Inf. Syst., vol. 64, no. 1, pp. 35–74, 2022.
S. Wu, F. Sun, W. Zhang, X. Xie, and B. Cui, Graph neural networks in recommender systems: A survey, ACM Comput. Surv., vol. 55, no. 5, p. 97, 2022.
F. Wang, H. Zhu, G. Srivastava, S. Li, M. R. Khosravi, and L. Qi, Robust collaborative filtering recommendation with user-item-trust records, IEEE Trans. Comput. Soc. Syst., vol. 9, no. 4, pp. 986–996, 2022.
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, 2023.
S. Ahmadian, N. Joorabloo, M. Jalili, and M. Ahmadian, Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach, Expert Syst. Appl., vol. 187, p. 115849, 2022.
O. A. Wahab, G. Rjoub, J. Bentahar, and R. Cohen, Federated against the cold: A trust-based federated learning approach to counter the cold start problem in recommendation systems, Inf. Sci., vol. 601, pp. 189–206, 2022.
J. Chen, W. Wu, L. Shi, W. Zheng, and L. He, Long-tail session-based recommendation from calibration, Appl. Intell., vol. 53, no. 4, pp. 4685–4702, 2023.
Z. Liu, L. Yang, Z. Fan, H. Peng, and P. S. Yu, Federated social recommendation with graph neural network, ACM Trans. Intell. Syst. Technol., vol. 13, no. 4, p. 55, 2022.
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., vol. 23, no. 3, pp. 1–19, 2023.
M. C. Chiu, G. J. Hwang, and L. H. Hsia, Promoting students’ artwork appreciation: An experiential learning-based virtual reality approach, Br. J. Educ. Technol., vol. 54, no. 2, pp. 603–621, 2023.
K. M. Darda and A. Chatterjee, The impact of contextual information on aesthetic engagement of artworks, Sci. Rep., vol. 13, no. 1, p. 4273, 2023.
N. Gelvez, Multi-agent system used for recommendation of historical and cultural memories, TECCIENCIA, vol. 14, no. 26, pp. 52–62, 2019.
L. Qi, J. Li, M. Elahi, K. Sood, Y. Yuan, and M. Khosravi, Guest editorial: Special issue on security and privacy for AI-powered smart IoT applications, Digit. Commun. Netw., vol. 8, no. 4, pp. 411–414, 2022.
J. Li, X. Luo, Y. Zhang, P. Zhang, C. Yang, and F. Liu, Extracting embedded messages using adaptive steganography based on optimal syndrome-trellis decoding paths, Digit. Commun. Netw., vol. 8, no. 4, pp. 455–465, 2022.
L. Qi, H. Song, X. Zhang, G. Srivastava, X. Xu, and S. Yu, Compatibility-aware web API recommendation for mashup creation via textual description mining, ACM Trans. Multimed. Comput. Commun. Appl., vol. 17, no. 1s, p. 20, 2021.
859
Views
269
Downloads
0
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
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/).
京公网安备11010802044758号