Author(s)

Shouliang Lai ¹, Min Sun ¹, Binrong Huang ¹

Affiliation(s)

¹ Collage of Packaging Design and Art, Hunan University of Technology, Zhuzhou, Hunan, China

Corresponding Author

Binrong Huang

ABSTRACT

With the development of economy, the Internet has developed rapidly as a new economic platform. At present, the Internet has become the best carrier of advertising and has huge commercial value. However, the extensive placement of advertisements not only brings troubles to Internet users, but also wastes the cost of advertisers. It cannot achieve the corresponding publicity effect, and cannot stimulate the consumer psychology of users, so the rate of return is low. This paper takes the network platform as the environment and data mining technology and digital twins as the technical support to study the dynamic clustering analysis advertising delivery algorithm and the collaborative filtering advertising delivery algorithm. This paper conducts experiments with KDD CUP 2012-Track 2 as the main dataset. The results show that the dynamic clustering analysis based on the K-means algorithm can greatly reduce the classification prediction error of users and film and television advertisements, and can better improve the accuracy of film and television advertisements. The error rate greater than 0.5 accounted for only 11.4% of the total number of advertisements. In the collaborative filtering algorithm, the performance of WSO is significantly better than that of the Slop One algorithm, and the error value becomes smaller when the number of test days is 15. At this time, the average MAE value of WSO is 0.916.

KEYWORDS

Digital Twin, Data Mining, Precise Placement of Film and Television Advertisements, Recommendation Prediction Algorithm

CITE THIS PAPER

Shouliang Lai, Min Sun, Binrong Huang, Design of Accurate Placement Method for Film and Television Advertisements Based on Digital Twin and Data Mining. Media and Communication Research (2023) Vol. 4: 21-34. DOI: http://dx.doi.org/10.23977/mediacr.2023.040904.

REFERENCES

[1] Slot M, Lutters E. Digital twinning for purpose-driven information management in production. Procedia CIRP, 2021, 100(3):666-671.
[2] J Wa ta da, Roy A, Vasant P. Preference Identification Based on Big Data Mining for Customer Responsibility Management. International Journal of Intelligent Technologies and Applied Statistics, 2020, 13(1):1-24.
[3] Tomeo P, I Fernández-Tobías, Cantador I, et al. Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2017, 25(Suppl. 2):57-78.
[4] Liu Y, Yang T, Qi T. An Attention-Based User Preference Matching Network for Recommender System. IEEE Access, 2020, 8(99):41100-41107.
[5] Wang X, Dai Z, Li H, et al. Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis. Complexity, 2021, 2021(2):1-11.
[6] Tan Z, H Yu, Wei W, et al. Top-K interesting preference rules mining based on MaxClique. Expert Systems with Application, 2020, 143(Apr.):113043.1-113043.18.
[7] Ge J, Shi L L, Liu L, et al. Intelligent Link Prediction Management Based on Community Discovery and User Behavior Preference in Online Social Networks. Wireless Communications and Mobile Computing, 2021, 2021(1):1-13.
[8] Xiao C, Qiao Y, Xia K, et al. Research on Tourist Routes Recommendation Based on the User Preference Drifting Over Time. International Journal of Computer Systems Science & Engineering, 2018, 33(2):95-103.
[9] Janiijevi S, Petrovi O, Stefanovi M. Sales prediction on e-commerce platform, by using data mining model. Serbian Journal of Engineering Management, 2020, 5(2):60-76.
[10] Tadeja S K, Seshadri P, Kristensson P O. AeroVR: An immersive visualisation system for aerospace design and digital twinning in virtual reality. Aeronautical Journal -New Series-, 2020, 124(1280):1-21.
[11] Hiskey T. Digital Twinning: The Future of Manufacturing?. Software World, 2017, 48(6):15-16.
[12] Berry C. Screening Post-1989 China: Critical Analysis of Chinese Film and Television. The China quarterly, 2017(230):553-554.
[13] D Zaborski, Ali M, Eyduran E, et al. Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms. Pakistan Journal of Zoology, 2019, 51(2):421-431.
[14] Deng B, Xu J, Wei X. Tourism Destination Preference Prediction Based on Edge Computing. Mobile Information Systems, 2021, 2021(1):1-11.
[15] Sharma P K, Rathore S, Park J H. Multilevel learning based modeling for link prediction and users' consumption preference in Online Social Networks. Future Generation Computer Systems, 2017, 93(APR.):952-961.
[16] Wang Q, Liu F, Huang P, et al. A Hierarchical Attention Model for CTR Prediction Based on User Interest. IEEE Systems Journal, 2020, 14(3):4015-4024.
[17] Chen H, Sun H, Cheng M, et al. A Recommendation Approach for Rating Prediction Based on User Interest and Trust Value. Computational Intelligence and Neuroscience, 2021, 2021(9):1-9.
[18] Castillo-Soriano M, Canavati-Espinosa A, Maldonado-Flores D I. O imaginário suburbano e o Mass Media: um reflexo de sua construo e desmontagem na gerao do chamado Baby Boom nos Estados Unidos (1946-1974). Revista Perspectivas, 2021, 6(1):6-23.
[19] Alvarezdionisi L E. Envisioning Skills for Adopting, Managing, and Implementing Big Data Technology in the 21st Century. International Journal of Information Technology & Computer Science, 2017, 9(1):18-25.
[20] Colombus C. BCE Monitors Hybrid Ops with Tektronix. TV technology, 2018, 36(11):34-34.

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