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Shopping list generation with machine learning

Tykesson, Daniel LU (2017) In LU-CS-EX 2017-31 EDA920 20171
Department of Computer Science
Abstract (Swedish)
When households do their grocery shopping some sort of shopping list is used
to make the shopping easier. The lists contain the groceries that are intended
for purchase. These lists can be boring to make and is also not free from errors,
so an automated way to generate these lists would be practical. This Master’s
thesis aims to generate these shopping lists with data from past grocery receipts
by predicting future receipts. We classified the groceries on the receipts
into categories that are organized into two layers of categories, 209 subcategories
and 17 main categories. These categories are modeled as time series
with an indicator variable that models purchase/no purchase in the category.
This indicator variable is estimated... (More)
When households do their grocery shopping some sort of shopping list is used
to make the shopping easier. The lists contain the groceries that are intended
for purchase. These lists can be boring to make and is also not free from errors,
so an automated way to generate these lists would be practical. This Master’s
thesis aims to generate these shopping lists with data from past grocery receipts
by predicting future receipts. We classified the groceries on the receipts
into categories that are organized into two layers of categories, 209 subcategories
and 17 main categories. These categories are modeled as time series
with an indicator variable that models purchase/no purchase in the category.
This indicator variable is estimated by using linear support vector machines
in combination with an intensity expectation. The quantity of the groceries
uses a Gaussian field as a model and is estimated with ordinary kriging. The
data contains 15,969 groceries, from 1,230 receipts and 34 households. The
quantity of a grocery on a receipt is measured by using the price paid for the
item. (Less)
Popular Abstract (Swedish)
Vid inhandling av livsmedel används ofta någon form av inköpslista. Dessa listor är inte
alltid tillgängliga, till exemepel på grund av att inhandlingen sker efter jobbet. Detta
examensarbetet presenterar en algorithm för att generera dessa listor med historisk
data.
Please use this url to cite or link to this publication:
author
Tykesson, Daniel LU
supervisor
organization
course
EDA920 20171
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
Machine learning, consumer prediction, consumer behavior, support vector machine, ordinary kriging
publication/series
LU-CS-EX 2017-31
report number
LU-CS-EX 2017-31
ISSN
1650-2884
language
English
id
8929400
date added to LUP
2018-01-17 18:26:04
date last changed
2018-01-17 18:26:04
@misc{8929400,
  abstract     = {When households do their grocery shopping some sort of shopping list is used
to make the shopping easier. The lists contain the groceries that are intended
for purchase. These lists can be boring to make and is also not free from errors,
so an automated way to generate these lists would be practical. This Master’s
thesis aims to generate these shopping lists with data from past grocery receipts
by predicting future receipts. We classified the groceries on the receipts
into categories that are organized into two layers of categories, 209 subcategories
and 17 main categories. These categories are modeled as time series
with an indicator variable that models purchase/no purchase in the category.
This indicator variable is estimated by using linear support vector machines
in combination with an intensity expectation. The quantity of the groceries
uses a Gaussian field as a model and is estimated with ordinary kriging. The
data contains 15,969 groceries, from 1,230 receipts and 34 households. The
quantity of a grocery on a receipt is measured by using the price paid for the
item.},
  author       = {Tykesson, Daniel},
  issn         = {1650-2884},
  keyword      = {Machine learning,consumer prediction,consumer behavior,support vector machine,ordinary kriging},
  language     = {eng},
  note         = {Student Paper},
  series       = {LU-CS-EX 2017-31},
  title        = {Shopping list generation with machine learning},
  year         = {2017},
}