Hybrid Method for User Feedback Analysis and Dynamic Weight Optimization of Key Parameters in AI Systems
Abstract
Understanding user preferences plays a crucial role in personalized services and intelligent systems. This understanding is achieved through feedback mechanisms in various forms, with natural language being the most preferred format. However, this approach requires precise identification of key features from user feedback and dynamic optimization of feature weights to enhance system decision-making accuracy. Traditional methods face significant limitations in extracting key feedback features from natural language texts and adapting weight distribution effectively. This paper presents a novel method for extracting key features from user feedback and distributing their weights based on preliminary multi-iteration interaction between the user and the AI system. This method integrates four main modules: YAKE-based feature extraction, personalized TF-IDF weight modeling, semantic fusion of vector representations with feature classification, and dynamic weight distribution for key features. Thus, a direct mapping mechanism is created from user feedback to the set of feature weights involved in building the decision-making model in AI systems. The novelty of the method lies in the development of a YAKE keyword extraction algorithm with improved semantic and feature density; a TF-IDF weight calculation algorithm integrating historical user preferences and weight personalization; a feature classification mechanism based on semantic similarity; and optimization of feature extraction and weight distribution processes. An emotional state prediction system with continuous data collection from 16 users over 30 days was used to test the method. The results showed that the proposed method achieves an emotion prediction accuracy of 78.4%, which is 23% higher than baseline methods. A significant increase in user satisfaction with the system's predictions and a substantial reduction in the time to achieve stable feature weight distribution are noted.
References
M. A. Vasyunin and A. A. Bakhman, "Natural language understanding technologies," in Proc. of Artificial Intelligence in Automated Control and Data Processing Systems, 27-28 Apr. 2022, Moscow, 2022, pp. 269-274 (in Russian).
D. Jannach and M. Jugovac, "Measuring the business value of recommender systems," ACM Trans. Manag. Inf. Syst., vol. 10, no. 4, pp. 1-23, 2019; doi:10.1145/3370082.
A. S. Tewari and A. G. Barman, "Collaborative recommendation system using dynamic content based filtering, association rule mining and opinion mining," Int. J. Intell. Eng. Syst., vol. 10, no. 5, pp. 57-66, 2017; doi:10.22266/ijies2017.1031.07.
G. Salton, A. Wong, and C. S. Yang, "A vector space model for automatic indexing," Commun. ACM, vol. 18, no. 11, pp. 613-620, 1975; doi:10.1145/361219.361220.
J. J. Rocchio Jr., "Relevance feedback in information retrieval," in The SMART Retrieval System: Experiments in Automatic Document Processing, Englewood Cliffs, NJ, USA: Prentice-Hall, 1971, pp. 313-323.
M. M. Ninichuk and D. E. Namiot, "Survey on methods for building session-based recommender systems," Int. J. Open Inf. Technol., vol. 11, no. 5, pp. 22-32, 2023 (in Russian).
J. Devlin et al., "BERT: Pre-training of deep bidirectional transformers for language understanding," in *Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)*, Minneapolis, MN, USA, 2019, vol. 1, pp. 4171-4186; doi:10.18653/v1/N19-1423.
R. Campos, V. Mangaravite, A. Pasquali, A. Jorge, C. Nunes, and A. Jatowt, "Yake! Collection-independent automatic keyword extractor," in Proc. of Advances in Information Retrieval (ECIR), Grenoble, France, Springer, Cham., 2018, pp. 806-810; doi:10.1007/978-3-319-76941-7_80.
S. Ruder, "An overview of gradient descent optimization algorithms," 2016. [Online]. Available: https://arxiv.org/abs/1609.04747.
J. H. Holland, "Genetic algorithms," Sci. Amer., vol. 267, no. 1, pp. 66-73, 1992; doi:10.1038/scientificamerican0792-66.
S. Robertson and H. Zaragoza, "The probabilistic relevance framework: BM25 and beyond," in Found. Trends Inf. Retrieval, vol. 3, no. 4, pp. 333-389, 2009; doi:10.1561/1500000019.
T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," 2013. [Online]. Available: https://arxiv.org/abs/1301.3781.
A. Vaswani et al., "Attention is all you need," in *Adv. Neural Inf. Process. Syst. (NeurIPS-2017)*, Long Beach, CA, USA, 2017, vol. 30, pp. 5998–6008.
J. Pennington, R. Socher, and C. D. Manning, "GloVe: Global vectors for word representation," in Proc. 2014 Conf. Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1532–1543; doi:10.3115/v1/D14-1162.
P. Bojanowski et al., "Enriching word vectors with subword information," Trans. Assoc. Comput. Linguistics, vol. 5, pp. 135–146, 2017; doi:10.1162/tacl_a_00051.
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