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Analysis of list creation with machine learning

Thunberg, Jonas LU and Khatib, Aminn (2024) EITL05 20241
Department of Electrical and Information Technology
Abstract (Swedish)
Detta examensarbete utforskar listgenerering med hjälp av maskininlärning inom IKEA. Avhandlingsarbetet involverade informationsinsamling, planering, studier av maskininlärning koncept, skapande av ett konceptbevis (POC) och analys av resultaten. Den tekniska bakgrunden byggdes på litteratur genomgångar och praktiska färdigheter genom maskininlärning kurser som Google's "Machine Learning for Beginners" och Kaggle's avancerade kurser. Metodiken innefattar kontinuerligt lärande och förbättring, med målet att förstå IKEA, data uppbyggnad och implementera maskininlärningsmodeller.

Författarna arbetade tillsammans varje steg på vägen, samarbetade för att utforma modellen, konstruktionen runt den och avhandlingen. Presentationen av resultaten... (More)
Detta examensarbete utforskar listgenerering med hjälp av maskininlärning inom IKEA. Avhandlingsarbetet involverade informationsinsamling, planering, studier av maskininlärning koncept, skapande av ett konceptbevis (POC) och analys av resultaten. Den tekniska bakgrunden byggdes på litteratur genomgångar och praktiska färdigheter genom maskininlärning kurser som Google's "Machine Learning for Beginners" och Kaggle's avancerade kurser. Metodiken innefattar kontinuerligt lärande och förbättring, med målet att förstå IKEA, data uppbyggnad och implementera maskininlärningsmodeller.

Författarna arbetade tillsammans varje steg på vägen, samarbetade för att utforma modellen, konstruktionen runt den och avhandlingen. Presentationen av resultaten för IKEA-kollegor gav insikter och möjligheter till ytterligare förbättringar. Genom att använda Python-verktyg som NumPy och Pandas och utvecklingsmiljön Visual Studio Code navigerade författarna den teoretiska kunskapen om maskininlärning och rekommendationssystem för att utveckla modellen för listgenerering. (Less)
Abstract
This Bachelor’s Thesis delves into the category of list generation using machine learning within IKEA. The thesis work involved information gathering, planning, studying machine learning concepts, creating a proof of concept (POC), and analyzing the results. The Technical Background was built upon literature reviews and practical skills acquired through Machine learning courses such as Google’s “Machine Learning for Beginners” and Kaggle’s advanced courses. The methodology involves continuous learning and improvement, with the goal being to understand IKEA, data landscapes and implementing machine learning models.

The authors worked together every step of the way, cooperating to craft the model, construction around it and the thesis.... (More)
This Bachelor’s Thesis delves into the category of list generation using machine learning within IKEA. The thesis work involved information gathering, planning, studying machine learning concepts, creating a proof of concept (POC), and analyzing the results. The Technical Background was built upon literature reviews and practical skills acquired through Machine learning courses such as Google’s “Machine Learning for Beginners” and Kaggle’s advanced courses. The methodology involves continuous learning and improvement, with the goal being to understand IKEA, data landscapes and implementing machine learning models.

The authors worked together every step of the way, cooperating to craft the model, construction around it and the thesis. The presentations of findings to IKEA colleagues provided insights and opportunities for more improvements. By using Python tools like NumPy and Pandas and the development environment Visual Studio Code, the authors navigated the theoretical knowledge of machine learning and recommendation systems to develop the model for list generation. (Less)
Please use this url to cite or link to this publication:
author
Thunberg, Jonas LU and Khatib, Aminn
supervisor
organization
course
EITL05 20241
year
type
M2 - Bachelor Degree
subject
keywords
Machine Learning, Matrix Factorization, Recommendation System, User Experience, List Generation
report number
LU/LTH-EIT 2024-995
language
English
id
9165537
date added to LUP
2024-06-20 14:59:10
date last changed
2024-06-20 14:59:10
@misc{9165537,
  abstract     = {{This Bachelor’s Thesis delves into the category of list generation using machine learning within IKEA. The thesis work involved information gathering, planning, studying machine learning concepts, creating a proof of concept (POC), and analyzing the results. The Technical Background was built upon literature reviews and practical skills acquired through Machine learning courses such as Google’s “Machine Learning for Beginners” and Kaggle’s advanced courses. The methodology involves continuous learning and improvement, with the goal being to understand IKEA, data landscapes and implementing machine learning models.

The authors worked together every step of the way, cooperating to craft the model, construction around it and the thesis. The presentations of findings to IKEA colleagues provided insights and opportunities for more improvements. By using Python tools like NumPy and Pandas and the development environment Visual Studio Code, the authors navigated the theoretical knowledge of machine learning and recommendation systems to develop the model for list generation.}},
  author       = {{Thunberg, Jonas and Khatib, Aminn}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Analysis of list creation with machine learning}},
  year         = {{2024}},
}