• A graph-based recommendation s...

Treffer: A graph-based recommendation system leveraging cosine similarity for enhanced marketing decisions

Title:
A graph-based recommendation system leveraging cosine similarity for enhanced marketing decisions
Authors:
Smutek, Tomasz, Kowalski, Marcin, Ivashko, Olena, Chmura, Robert, Sokołowska-Woźniak, Justyna
Publisher Information:
University of Piraeus. International Strategic Management Association
Publication Year:
2024
Collection:
University of Malta: OAR@UM / L-Università ta' Malta
Subject Terms:
Marketing -- Decision making -- Data processing, Consumer behavior -- Data processing, Customer relations -- Management, Recommender systems (Information filtering)
Document Type:
Fachzeitschrift article in journal/newspaper
Language:
English
Relation:
https://www.um.edu.mt/library/oar/handle/123456789/122609
DOI:
10.35808/ersj/3389
Availability:
https://www.um.edu.mt/library/oar/handle/123456789/122609
https://doi.org/10.35808/ersj/3389
Rights:
info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.
Accession Number:
edsbas.943B15AD
Database:
BASE

Weitere Informationen

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. ; peer-reviewed