With the Internet and logistics transportation developing rapidly and the popularization of devices like smartphones, online shopping has become an ordinary issue for each of us. No matter what product is needed, such as a refrigerator or just a spoon, could be available as long as we search in the website or application of the e-commerce platform, fill out an address and then pay for the product. While browsing in the e-commerce platform for the goods wanted, it is just as though we are in a large market which involves kinds of products. Consumers need to find what they want. The retailers and the e-commerce platform also need to reach for more potential customers and get more orders more efficiently. This is the reason for the adoption of recommendation algorithms. Although recommendation algorithms have somewhat achieved these in practice, there are still a few problems. This paper mainly described the application status of the intelligent recommendation algorithm of the E-commerce platform and how the cocoon room came into being in such a situation. Then there was an analysis of the cocoon room of the intelligent recommendation algorithm, making consumers feel bored or regret after purchasing and consequently harming the interests of the retailers and the platform itself. Subsequently, solutions to the two kinds of negative moods were proposed. The last is the summary, meaning in practice, and extended study direction of this analysis.
| Published in | American Journal of Applied Psychology (Volume 11, Issue 6) |
| DOI | 10.11648/j.ajap.20221106.14 |
| Page(s) | 167-171 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
E-commerce Platform, Recommendation Algorithm, Cocoon Room, Negativte Moods
| [1] | Davenport T, Guha A, Grewal D, et al. (2020). How artificial intelligence will change the future of marketing. J. Journal of the Academy of Marketing Science, 48. |
| [2] | Q André, Carmon Z, Wertenbroch K, et al. (2018). Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data. J. Customer Needs & Solutions, 5 (1-2): 28-37. |
| [3] | Liu. J. L, Li. X. G. (2020) Techniques for Recommendation system: A Survey. J. Computer Science, 47 (7): 47-55. |
| [4] | Byeong Man Kim et al. (2006). A new approach for combining content-based and collaborative filters. J. Intell. Inf. Syst., 27 (1): 79-91. |
| [5] | Kim, BD., Kim, SO. (2001). A new recommender system to combine content-based and collaborative filtering systems. J. Database Mark Cust Strategy Manag 8, 244–252. |
| [6] | Prem Melville, Raymond J. Mooney, Ramadass Nagarajan. (2002). Content-Boosted Collaborative Filtering for Improved Recommendations. J. American Association for Artificial Intelligence, 23: 187-192. |
| [7] | Balabanovic, M., Shoham, Y. (1997). Fab: Content-based, collaborative recommendation. J. Communications of the ACM 40 (3), 66–72. |
| [8] | Menon, S., & Kahn, B. E. (1995). The Impact of Context on Variety Seeking in Product Choices. Journal of Consumer Research, 22 (3), 285. |
| [9] | Zeelenberg, M., Beattie, J., Pligt, J. V. and Vries, N. K. (1996). Consequences of Regret Aversion: Effects of Expected Feedback on Risky Decision Making. J. Organizational Behavior and Human Decision Processes, 65 (2): 148-158. |
| [10] | Zeelenberg, M., van Dijk, W. W., Manstead, A. S. R., and van der Pligt. (1998). The experience of regret and disappointment. J. Cognition and Emotion, 12, 221–230. |
| [11] | Venkatesan, R. and Kumar, V. (2004). A Customer Lifetime Value Framework for Customer Selection and Resource Allocation Strategy. J. Journal of Marketing, 68 (4): 106-125. |
| [12] | Zhu. G. W, Gao. W. L, liu. J. H, li. S. F and Lu. J. F. (2021). Artificial Intelligence Marketing: A Research Review and Prospect. J. Foreign Economics & Management., 43 (07): 86-96. |
| [13] | Qian. M. H and Xu. Z. X. (2019). A Study of Dynamic Recognition of Consumer Brand Decision-making Preference Based on Machine Learning Method. J. Nankai Business Review. 22 (03): 66-76. |
| [14] | Li. X. Q and Zhang. X. X. (2020). People-oriented: Personalized Recommendations in the Era of Artificial Intelligence. J. Journal of Shanghai University of International Business and Economics. 27 (04): 90-99. |
| [15] | Wang. Q. Q and Yang. D. (2019). Research on localized intelligent marketing strategy under the background of artificial intelligence. J. Journalism Lover. (11): 55-59. |
| [16] | He. Y. Q, Tang. Y. Y and Zhang. J. F. (2016). Consumer Psychological Reactance: A Literature Review and Prospects. J. Foreign Economics & Management., 38 (02): 49-61. |
APA Style
Jingjing Huang. (2022). Influences and Solutions of the Cocoon Room Caused by the Intelligent Recommendation Algorithm of the E-commerce Platform. American Journal of Applied Psychology, 11(6), 167-171. https://doi.org/10.11648/j.ajap.20221106.14
ACS Style
Jingjing Huang. Influences and Solutions of the Cocoon Room Caused by the Intelligent Recommendation Algorithm of the E-commerce Platform. Am. J. Appl. Psychol. 2022, 11(6), 167-171. doi: 10.11648/j.ajap.20221106.14
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.ajap.20221106.14,
author = {Jingjing Huang},
title = {Influences and Solutions of the Cocoon Room Caused by the Intelligent Recommendation Algorithm of the E-commerce Platform},
journal = {American Journal of Applied Psychology},
volume = {11},
number = {6},
pages = {167-171},
doi = {10.11648/j.ajap.20221106.14},
url = {https://doi.org/10.11648/j.ajap.20221106.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajap.20221106.14},
abstract = {With the Internet and logistics transportation developing rapidly and the popularization of devices like smartphones, online shopping has become an ordinary issue for each of us. No matter what product is needed, such as a refrigerator or just a spoon, could be available as long as we search in the website or application of the e-commerce platform, fill out an address and then pay for the product. While browsing in the e-commerce platform for the goods wanted, it is just as though we are in a large market which involves kinds of products. Consumers need to find what they want. The retailers and the e-commerce platform also need to reach for more potential customers and get more orders more efficiently. This is the reason for the adoption of recommendation algorithms. Although recommendation algorithms have somewhat achieved these in practice, there are still a few problems. This paper mainly described the application status of the intelligent recommendation algorithm of the E-commerce platform and how the cocoon room came into being in such a situation. Then there was an analysis of the cocoon room of the intelligent recommendation algorithm, making consumers feel bored or regret after purchasing and consequently harming the interests of the retailers and the platform itself. Subsequently, solutions to the two kinds of negative moods were proposed. The last is the summary, meaning in practice, and extended study direction of this analysis.},
year = {2022}
}
TY - JOUR T1 - Influences and Solutions of the Cocoon Room Caused by the Intelligent Recommendation Algorithm of the E-commerce Platform AU - Jingjing Huang Y1 - 2022/12/08 PY - 2022 N1 - https://doi.org/10.11648/j.ajap.20221106.14 DO - 10.11648/j.ajap.20221106.14 T2 - American Journal of Applied Psychology JF - American Journal of Applied Psychology JO - American Journal of Applied Psychology SP - 167 EP - 171 PB - Science Publishing Group SN - 2328-5672 UR - https://doi.org/10.11648/j.ajap.20221106.14 AB - With the Internet and logistics transportation developing rapidly and the popularization of devices like smartphones, online shopping has become an ordinary issue for each of us. No matter what product is needed, such as a refrigerator or just a spoon, could be available as long as we search in the website or application of the e-commerce platform, fill out an address and then pay for the product. While browsing in the e-commerce platform for the goods wanted, it is just as though we are in a large market which involves kinds of products. Consumers need to find what they want. The retailers and the e-commerce platform also need to reach for more potential customers and get more orders more efficiently. This is the reason for the adoption of recommendation algorithms. Although recommendation algorithms have somewhat achieved these in practice, there are still a few problems. This paper mainly described the application status of the intelligent recommendation algorithm of the E-commerce platform and how the cocoon room came into being in such a situation. Then there was an analysis of the cocoon room of the intelligent recommendation algorithm, making consumers feel bored or regret after purchasing and consequently harming the interests of the retailers and the platform itself. Subsequently, solutions to the two kinds of negative moods were proposed. The last is the summary, meaning in practice, and extended study direction of this analysis. VL - 11 IS - 6 ER -
Email: