Personalized recognition system in online shopping by using deep learning

Authors

  • Manjula Devarakonda Venkata Pragati Engineering College
  • Prashanth Donda CVR College of Engineering
  • N. Bindu Madhavi Foundation University image/svg+xml
  • Pavitar Parkash Singh Lovely Professional University image/svg+xml
  • A. Azhagu Jaisudhan Pazhani Ramco Institute of Technology
  • Shaik Rehana Banu Vasavi Institute of Management and Computer Science

DOI:

https://doi.org/10.4108/eetiot.4810

Keywords:

Customer emotions, conventional analysis, customer experience, Deep learning

Abstract

This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.

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References

Generosi, Andrea & Ceccacci, Silvia & Mengoni, Maura. (2018). A deep learning-based system to track and analyze customer behavior in retail store. 1-6. 10.1109/ICCE-Berlin.2018.8576169. DOI: https://doi.org/10.1109/ICCE-Berlin.2018.8576169

Fu, Z.; He, X.; Wang, E.; Huo, J.; Huang, J.; Wu, D. Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning. Sensors 2021, 21, 885. https://doi.org/10.3390/s21030885 DOI: https://doi.org/10.3390/s21030885

Yuchen Wei, Son Tran, Shuxiang Xu, Byeong Kang, Matthew Springer, "Deep Learning for Retail Product Recognition: Challenges and Techniques", Computational Intelligence and Neuroscience, vol. 2020, Article ID 8875910, 23 pages, 2020. https://doi.org/10.1155/2020/8875910

Wei, Yuchen & Tran, Son & Xu, Shuxiang & Kang, Byeong & Springer, Matthew. (2020). Deep Learning for Retail Product Recognition: Challenges and Techniques. Computational Intelligence and Neuroscience. 2020. 1-23. 10.1155/2020/8875910. DOI: https://doi.org/10.1155/2020/8875910

Hong, T.; Choi, J.-A.; Lim, K.; Kim, P. Enhancing Personalized Ads Using Interest Category Classification of SNS Users Based on Deep Neural Networks. Sensors 2021, 21, 199. https://doi.org/10.3390/s21010199 DOI: https://doi.org/10.3390/s21010199

Sarker, I.H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). https://doi.org/10.1007/s42979-021-00592-x DOI: https://doi.org/10.1007/s42979-021-00592-x

Peng Zhang, E-commerce products recognition based on a deep learning architecture: Theory and implementation, Future Generation Computer Systems, Volume 125, 2021, Pages 672-676, ISSN 0167-739X, https://doi.org/10.1016/j.future.2021.06.058. (https://www.sciencedirect.com/science/article/pii/S0167739X21002533) DOI: https://doi.org/10.1016/j.future.2021.06.058

Burns D, Boyer P, Arrowsmith C, Whyne C. Personalized Activity Recognition with Deep Triplet Embeddings. Sensors (Basel). 2022 Jul 13;22(14):5222. doi: 10.3390/s22145222. PMID: 35890902; PMCID: PMC9324610. DOI: https://doi.org/10.3390/s22145222

Tian, D, Zhao, R, Ma, R, et al. MDCD: A malware detection approach in cloud using deep learning. Trans Emerging Tel Tech. 2022; 33( 11):e4584. doi:10.1002/ett.4584 DOI: https://doi.org/10.1002/ett.4584

Miyazato, T, Uehara, W, Nagayama, I. Development of a free viewpoint pedestrian recognition system using deep learning for multipurpose flying drone. Electron Comm Jpn. 2019; 102: 16– 24. https://doi.org/10.1002/ecj.12215 DOI: https://doi.org/10.1002/ecj.12215

Alzubaidi, L., Zhang, J., Humaidi, A.J. et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8 DOI: https://doi.org/10.1186/s40537-021-00444-8

BramahHazela, J. Hymavathi, T. Rajasanthosh Kumar, S. Kavitha, D. Deepa, Sachin Lalar, Prabakaran Karunakaran, "Machine Learning: Supervised Algorithms to Determine the Defect in High-Precision Foundry Operation", Journal of Nanomaterials, vol. 2022, Article ID 1732441, 9 pages, 2022. https://doi.org/10.1155/2022/1732441 DOI: https://doi.org/10.1155/2022/1732441

Aluri, Aj & Price, Bradley & Mcintyre, Nancy. (2018). Using Machine Learning To Cocreate Value Through Dynamic Customer Engagement In A Brand Loyalty Program. Journal of Hospitality & Tourism Research. 43. 109634801775352. 10.1177/1096348017753521. DOI: https://doi.org/10.1177/1096348017753521

Patankar, Nikhil & Dixit, Soham & Bhamare, Akshay & Darpel, Ashutosh & Raina, Ritik. (2021). Customer Segmentation Using Machine Learning. 10.3233/APC210200.

Patankar, Nikhil & Dixit, Soham & Bhamare, Akshay & Darpel, Ashutosh & Raina, Ritik. (2021). Customer Segmentation Using Machine Learning. 10.3233/APC210200. DOI: https://doi.org/10.3233/APC210200

Samyuktha Palangad Othayoth and Raja Muthalagu. 2022. Customer segmentation using various machine learning techniques. Int. J. Bus. Intell. Data Min. 20, 4 (2022), 480–496. https://doi.org/10.1504/ijbidm.2022.123218 DOI: https://doi.org/10.1504/IJBIDM.2022.123218

Chenguang Wang, Efficient customer segmentation in digital marketing using deep learning with swarm intelligence approach, Information Processing & Management, Volume 59, Issue 6, 2022, 103085, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2022.103085. (https://www.sciencedirect.com/science/article/pii/S0306457322001868) DOI: https://doi.org/10.1016/j.ipm.2022.103085

Sruthi Janardhanan and Raja Muthalagu 2020 J. Phys.: Conf. Ser. 1706 012160 DOI: https://doi.org/10.1088/1742-6596/1706/1/012160

Chinedu Pascal Ezenkwu, Simeon Ozuomba and Constance kalu, “Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 4(10), 2015. http://dx.doi.org/10.14569/IJARAI.2015.041007 DOI: https://doi.org/10.14569/IJARAI.2015.041007

Alkhayrat, M., Aljnidi, M. & Aljoumaa, K. A comparative dimensionality reduction study in telecom customer segmentation using deep learning and PCA. J Big Data 7, 9 (2020). https://doi.org/10.1186/s40537-020-0286-0 DOI: https://doi.org/10.1186/s40537-020-0286-0

Li, Xutong, and Rongheng Lin. "Speech Emotion Recognition for Power Customer Service." 2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021. DOI: https://doi.org/10.1109/ICCC54389.2021.9674619

Chuttur, Yasser, and Reean Tencamah. "Analysing and Plotting Online Customer Emotions Using a Lexicon-Based Approach." Soft Computing and Signal Processing: Proceedings of 3rd ICSCSP 2020, Volume 1. Springer Singapore, 2021.

Xu, Anbang, et al. "A new chatbot for customer service on social media." Proceedings of the 2017 CHI conference on human factors in computing systems. 2017. DOI: https://doi.org/10.1145/3025453.3025496

Li, Rui, et al. "Sentiment mining of online reviews of peer-to-peer accommodations: Customer emotional heterogeneity and its influencing factors." Tourism Management 96 (2023): 104704. DOI: https://doi.org/10.1016/j.tourman.2022.104704

Magids, Scott, Alan Zorfas, and Daniel Leemon. "The new science of customer emotions." Harvard Business Review 76.11 (2015): 66-74.

Kong, Yuqiang, and Yaoping He. "Customer service system design based on big data machine learning." Journal of Physics: Conference Series. Vol. 2066. No. 1. IOP Publishing, 2021. DOI: https://doi.org/10.1088/1742-6596/2066/1/012017

Sidaoui, Karim, Matti Jaakkola, and Jamie Burton. "AI feel you: customer experience assessment via chatbot interviews." Journal of Service Management 31.4 (2020): 745-766. DOI: https://doi.org/10.1108/JOSM-11-2019-0341

Chuttur, Yasser, and Nandishta Rawoteea. "Time Series Visualization of Customer Emotions Using Artificial Neural Network." Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2020. Springer Singapore, 2021. DOI: https://doi.org/10.1007/978-981-33-4299-6_9

Kausalliya, R., Barakkath Nisha Usman, and R. Yasir Abdullah. "An Efficient Approach for Customer Emotion Analytics Using Deep Learning." Proceedings of Third International Conference on Intelligent Computing, Information and Control Systems: ICICCS 2021. Singapore: Springer Nature Singapore, 2022. DOI: https://doi.org/10.1007/978-981-16-7330-6_47

Huang, Zheng-Wei, Yang-Yang Liu, and Yi-Ting Tan. "A Deep Learning Model for Sentiment Analysis of Online Customer Service Dialogue." Available at SSRN 4242759.

Gill, Rupali, and Jaiteg Singh. "A Proposed LSTM‐Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG." Advanced Analytics and Deep Learning Models (2022): 181-206. DOI: https://doi.org/10.1002/9781119792437.ch8

Dhotre, Virendrakumar Anna, et al. "Big data analytics using MapReduce for education system." Linguistica Antverpiensia (2021): 3130-3138.

Shoukry, Alaa, and Fares Aldeek. "Attributes prediction from IoT consumer reviews in the hotel sectors using conventional neural network: deep learning techniques." Electronic Commerce Research 20 (2020): 223-240. DOI: https://doi.org/10.1007/s10660-019-09373-4

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Published

10-01-2024

How to Cite

[1]
M. Devarakonda Venkata, P. Donda, N. B. Madhavi, P. Parkash Singh, A. A. Jaisudhan Pazhani, and S. Rehana Banu, “Personalized recognition system in online shopping by using deep learning”, EAI Endorsed Trans IoT, vol. 10, Jan. 2024.

Issue

Vol. 10 (2024): EAI Endorsed Transactions on Internet of Things

Section

Short communication

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