Integrating Self-Attention Mechanisms For Contextually Relevant Information In Product Management
DOI:
https://doi.org/10.22399/ijcesen.651Keywords:
Product Management, NLP, Transformer Architecture, Translation Services, Product Quality EnhancementAbstract
GPT-Product is an innovative AI solution that aims to transform product management and development by using sophisticated natural language processing (NLP) abilities. Building on Transformer architecture, frameworks like as BERT, GPT, and T5 have greatly enhanced AI applications, thereby allowing more efficient chatbots, and translation services, content generating tools, and so on. GPT-Product utilises the advanced GPT-3.5 architecture to provide full solutions for market evaluation, interpretation of client input, and automated content development. This enhances decision-making processes. This product utilises the self-attention mechanism of the Transformer model to provide precise and contextually appropriate information, enabling effective management of the product lifetime. GPT-Product uses deep learning to optimise processes, decrease time-to-market, and enhance product quality. It positions itself as an essential tool for firms striving to maintain competitiveness in a rapidly changing industry.
References
Yin, Y. (2024, June 23). Cultural Product Design Concept Generation with Symbolic Semantic Information ExpressionUsingGPT. https://doi.org/10.21606/drs.2024.508
Gu, K., Lee, J. H., Shin, J., Hwang, J. A., Min, J. H., Jeong, W. K., Lee, M. W., Song, K. D., & Bae, S. H. (2024). Using GPT-4 for LI-RADS feature extraction and categorization with multilingual free-text reports. Liver International, 44(7),1578–1587. https://doi.org/10.1111/liv.15891
Bdoor, S. Y., & Habes, M. (2024). Use Chat GPT in Media Content Production Digital Newsrooms Perspective. In Studies in Big Data (Vol. 144, pp. 545–561). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-52280-2_34
Ouyang, T., MaungMaung, A., Konishi, K., Seo, Y., & Echizen, I. (2024). Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance. http://arxiv.org/abs/2401.07441
Guo, T. (2024). Improving Text Generation for Product Description by Label Error Correction. TechRxiv. https://doi.org/10.36227/techrxiv.171198068.89981260/v1
Grandi, D., Jain, Y. P., Groom, A., Cramer, B., & McComb, C. (2024). Evaluating Large Language Models for Material Selection. http://arxiv.org/abs/2405.03695
Kirshner, S. N. (2024). GPT and CLT: The impact of ChatGPT’s level of abstraction on consumer recommendations. Journal of Retailing and Consumer Services, 76. https://doi.org/10.1016/j.jretconser.2023.103580
Wahsheh, F. R., Moaiad, Y. al, Baker El-Ebiary, Y. A., Amir Fazamin Wan Hamzah, W. M., Yusoff, M. H., & Pandey, B. (2023). E-Commerce Product Retrieval Using Knowledge from GPT-4. 2023 International Conference on Computer Science and Emerging Technologies, CSET 2023. https://doi.org/10.1109/CSET58993.2023.10346860
Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. K. (2024). Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems. In Software 3(1);62–80).MDPIAG. https://doi.org/10.3390/software3010004
Li, L., Zhang, Y., & Chen, L. (2023). Prompt Distillation for Efficient LLM-based Recommendation. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. CIKM ’23: The 32nd ACM International Conference on Information and KnowledgeManagement.ACM. https://doi.org/10.1145/3583780.3615017
Shi, G., Deng, X., Luo, L., Xia, L., Bao, L., Ye, B., Du, F., Pan, S., & Li, Y. (2024). LLM-Powered Explanations: Unraveling Recommendations Through Subgraph Reasoning(Version2). arXiv. https://doi.org/10.48550/ARXIV.2406.15859
Soviero, B., Kuhn, D., Salle, A., Moreira, V.P. (2024). ChatGPT Goes Shopping: LLMs Can Predict Relevance in eCommerce Search. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14611. Springer, Cham. https://doi.org/10.1007/978-3-031-56066-8_1
Wang, H., & Na, T. (2023). Rethinking E-Commerce Search. In ACM SIGIR Forum 57(2);1–19). Association for Computing Machinery(ACM). https://doi.org/10.1145/3642979.3643007
Li, Y., Ma, S., Wang, X., Huang, S., Jiang, C., Zheng, H.-T., Xie, P., Huang, F., & Jiang, Y. (2024). EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 18582-18590. https://doi.org/10.1609/aaai.v38i17.29820
Sara, Fatima. (2024). Data-driven E-commerce: The Intersection of Sentiment Analysis, Causal Reasoning, and LLMs. 10.13140/RG.2.2.13655.48806.
Ngo Tran, G.T., Le Dinh, T., Pham-Nguyen, C. (2024). BABot: A Framework for the LLM-Based Chatbot Supporting Business Analytics in e-Commerce. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2024. Lecture Notes in Computer Science(),vol14810.Springer,Cham. https://doi.org/10.1007/978-3-031-70816-9_15
Guven, M. (2024). A Comprehensive Review of Large Language Models in Cyber Security. International Journal of Computational and Experimental Science and Engineering, 10(3);507-516. https://doi.org/10.22399/ijcesen.469
Adem, A. İrem, Turhan, Çiğdem, & Sezen, A. (2024). Systematic Mapping Study on Natural Language Processing for Social Robots. International Journal of Computational and Experimental Science and Engineering, 10(4);560-567. https://doi.org/10.22399/ijcesen.341
R. Dineshkumar, A. Ameelia Roseline, Tatiraju V. Rajani Kanth, J. Nirmaladevi, & G. Ravi. (2024). Adaptive Transformer-Based Multi-Modal Image Fusion for Real-Time Medical Diagnosis and Object Detection. International Journal of Computational and Experimental Science and Engineering, 10(4);890-897. https://doi.org/10.22399/ijcesen.562
AY, S. (2024). The Use of Agile Models in Software Engineering: Emerging and Declining Themes. International Journal of Computational and Experimental Science and Engineering, 10(4);1242-1248. https://doi.org/10.22399/ijcesen.703
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