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Automatiska rekommendationer i butik
University of Borås, Faculty of Librarianship, Information, Education and IT.
University of Borås, Faculty of Librarianship, Information, Education and IT.
2015 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Automatic recommendations in retail (English)
Abstract [sv]

Detaljhandeln i fysiska butiker är utsatt av konkurrens från en betydligt mer innovationsrik e-handel och har därför ett behov av att vidareutvecklas. Ett sätt för detaljhandeln att utvecklas är att utnyttja tekniker som visats fungera bra inom e-handeln. Rekommendationssystem som ger rekommendationer till sina användare har nått stora framgångar och används av i stort sett alla företag inom e-handeln. Den mest använda tekniken för att ta fram rekommendationer kallas för collaborative filtering. Inom detaljhandel används dock inte detta i någon större utsträckning. Det finns därför förhållandevis lite kunskap om vad kunder anser om rekommendationer i butik.

Syftet med studien är därför att utvärdera hur ett rekommendationssystem baserat på collaborative filtering presterar i en fysisk butik. Utvärderingen sker genom att mäta träffsäkerheten på rekommendationerna kunder får i en butik samt vad kunderna anser om dessa. Studien ämnar även att ta reda på hur kunder förhåller sig till automatiska rekommendationer i butik.

I studien används två forskningsmetodiker för att uppnå dess forskningsmål. Design science har tillämpats för att utvärdera hur ett rekommendationssystem baserat på collaborative filtering presterar i en fysisk butik. En prototyp baserat på collaborative filtering utvecklades för att generera rekommendationer. Prototypen användes sedan i ett användartest som genomfördes i en butiksmiljö. För att belysa hur kunder förhåller sig till automatiska rekommendationer i butik användes en enkätundersökning som utfördes i samband med studiens användartest.

Studiens resultat visar att prototypen gav rekommendationer med en hög träffsäkerhet där deltagarna upplevde rekommendationerna som bra och relevanta. Resultaten visar även att deltagarna i studien var positivt inställda till att få rekommendationer i butik. Detta leder till slutsatsen att rekommendationssystem baserat på collaborative filtering kan prestera väl i butiker vilket ger en indikation om att detta kan vara ett sätt för butiker att vidareutveckla handeln.

Abstract [en]

Retail stores are challenged by competition from the more innovative retailers in e-commerce and thus needs to adapt and evolve in order to stay competitive. This could be accomplished by using technology which has been proven successful in e-commerce. Recommender systems that produces recommendations to its users has been used successfully and is used by essentially all businesses involved in e-commerce. The most common method employed in these recommender systems is called collaborative filtering. Recommender systems have however not yet found its way into retail stores to a greater extent. This has led to a gap in knowledge regarding customer’s opinions of recommendations in retail stores.

The purpose of this study is therefore to evaluate how recommender system based on collaborative filtering performs when used in retail stores. The evaluation is performed by measuring the accuracy of the recommendations a customer receives in a retail store as well as what the customer thinks of the recommendation. This study also intends to explore and shed light on people’s opinions concerning automatic recommendations in retail stores.

Two different research methods have been used in this study. Design science is being used in order to evaluate how a recommender system based on collaborative filtering performs when used in retail stores. A prototype based on collaborative filtering was developed in order to generate recommendations. The prototype was then used in a user-test taking place in a retail-like environment. In order to shed light on people’s opinions regarding automatic recommendations in retail stores a questionnaire was handed out to the participants in conjunction with the user-test.

The results of the study show that the prototype could produce high accuracy recommendations where the participants perceived the recommendations as good and relevant. The results also show that the participants of the study have positive attitude and were in favor of receiving automatic recommendations in retail stores. This leads to the conclusion that recommendations based on collaborative filtering could indeed perform well in retail stores. This indicates that recommender systems using collaborative filtering is one possible way for retail stores to evolve their business.

Place, publisher, year, edition, pages
2015. , 54 p.
Keyword [en]
collaborative filtering, retail, recommender systems, recommendations
Keyword [sv]
collaborative filtering, butik, rekommendationssystem, rekommendationer
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:hb:diva-1031OAI: oai:DiVA.org:hb-1031DiVA: diva2:867439
Subject / course
Informatics
Uppsok
Technology
Supervisors
Examiners
Available from: 2015-11-06 Created: 2015-11-05 Last updated: 2015-11-12Bibliographically approved

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2015KSAI01(2314 kB)247 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • rtf