Subject Classification of Texts in Polish - from TF-IDF to Transformers

Walkowiak, Tomasz

doi:10.1007/978-3-030-76773-0_44

Cited by 6 publications

(1 citation statement)

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“…For the K-means clustering training phase, the process begins by importing the necessary libraries and mounting Google Drive to access and save files. Global variables for TF-IDF transformers are defined, and a function to transform input data using pre-trained TF-IDF transformers is implemented [47].…”

Section: K-means Clustering Trainingmentioning

confidence: 99%

Precision-Driven Product Recommendation Software: Unsupervised Models, Evaluated by GPT-4 LLM for Enhanced Recommender Systems

Roumeliotis,

Tselikas,

Nasiopoulos

2024

Software

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This paper presents a pioneering methodology for refining product recommender systems, introducing a synergistic integration of unsupervised models—K-means clustering, content-based filtering (CBF), and hierarchical clustering—with the cutting-edge GPT-4 large language model (LLM). Its innovation lies in utilizing GPT-4 for model evaluation, harnessing its advanced natural language understanding capabilities to enhance the precision and relevance of product recommendations. A flask-based API simplifies its implementation for e-commerce owners, allowing for the seamless training and evaluation of the models using CSV-formatted product data. The unique aspect of this approach lies in its ability to empower e-commerce with sophisticated unsupervised recommender system algorithms, while the GPT model significantly contributes to refining the semantic context of product features, resulting in a more personalized and effective product recommendation system. The experimental results underscore the superiority of this integrated framework, marking a significant advancement in the field of recommender systems and providing businesses with an efficient and scalable solution to optimize their product recommendations.

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Section: K-means Clustering Trainingmentioning

confidence: 99%