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A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions

Author

Listed:
  • Tomasz Smutek
  • Marcin Kowalski
  • Olena Ivashko
  • Robert Chmura
  • Justyna Sokolowska-Wozniak

Abstract

Purpose: This work aims to present a comprehensive customer recommendation system based on cosine similarity. The primary objective is to develop an effective tool that assists sellers in identifying and recommending similar customers by analyzing their characteristics and behaviors. Design/Methodology/Approach: The methodology analyzes demographic data, purchase history, and other customer characteristics to calculate cosine similarity. This process includes data processing techniques such as feature integration and generating a cosine similarity matrix. The results demonstrate the system's effectiveness through thorough analysis. Findings: The analysis confirms the effectiveness of the proposed recommendation system, revealing that using cosine similarity can identify and recommend similar customers accurately. Practical Implications: The study emphasizes incorporating modern data analysis methods into marketing and customer relationship management. This approach can enhance the efficiency of sales activities and elevate customer satisfaction. Originality/Value: This work offers a novel approach to customer recommendations by employing cosine similarity and innovative data processing techniques. It demonstrates how advanced data analysis methods can be leveraged to improve sales strategies and foster stronger customer relationships.

Suggested Citation

  • Tomasz Smutek & Marcin Kowalski & Olena Ivashko & Robert Chmura & Justyna Sokolowska-Wozniak, 2024. "A Graph-Based Recommendation System Leveraging Cosine Similarity for Enhanced Marketing Decisions," European Research Studies Journal, European Research Studies Journal, vol. 0(Special A), pages 83-93.
    • Handle: RePEc:ers:journl:v:xxvii:y:2024:i:speciala:p:83-93
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    References listed on IDEAS

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    1. Vicky Zampeta & Gregory Chondrokoukis, 2023. "A Comprehensive Approach through Robust Regression and Gaussian/Mixed-Markov Graphical Models on the Example of Maritime Transportation Accidents: Evidence from a Listed-in-NYSE Shipping Company," JRFM, MDPI, vol. 16(3), pages 1-30, March.
    2. Monika Kulisz & Grzegorz Kłosowski & Tomasz Rymarczyk & Jolanta Słoniec & Konrad Gauda & Wiktor Cwynar, 2024. "Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors," Energies, MDPI, vol. 17(3), pages 1-17, January.
    3. Eleftherios I. Thalassinos & Bozhana Venediktova & Daniela Staneva-Petkova & Vicky Zampeta, 2013. "Way of Banking Development Abroad: Branches or Subsidiaries," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 69-78.
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    More about this item

    Keywords

    Recommendation system; cosine similarity; customer behavior analysis.;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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