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Building a Sporting Goods Recommendation System
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis report describes an attempt to build a recommender system for recommending sporting goods in an e-commerce setting, using the customer purchase history as the input dataset. Two input datasets were considered, the item purchases dataset and the item-category dataset. Both the datasets are implicit, that is not explicitly rated by the customer. The data is also very sparse that very few users have purchased more than a handful of the items featured in the dataset. The report describes a method for dealing with both the implicit datasets as well as addressing the problem of sparsity. The report introduces SVD (Single Value Decomposition) with matrix factorization as a implementation for recommendation systems. Specifically implementations in the Apache Mahout machine learning framework.

Abstract [sv]

Denna rapport beskriver ett tillvägagångssätt för att med kundernas köphistorik bygga ett rekommendationssystem för rekommendation av sportprodukter på en e-handelsplats. Två olika datamängder behandlas, köphistorik per produkt och kund, samt köpfrekvensen per produktkategori per kund i köphistoriken. Båda är implicita datamängder, vilket betyder att kunderna inte har explicit uttryckt en åsikt för eller emot produkten, utan implicit uttrycker preferens genom sitt köp. Datan är även mycket gles, vilket betyder att den enskilda kunden generellt bara köpt en liten del av den totala mängden av sålda varor. Rapporten behandlar en metod som behandlar både den implicita karaktären av data och gleshets problemet. Rapporten introducerar SVD (Single Value Decomposition) med matrisfaktorisering som en metod för att implementera rekommendationssystem. Specifikt implementerat med hjälp av maskininlärningsbiblioteket Apache Mahout.

Place, publisher, year, edition, pages
2015. , 26 p.
Keyword [en]
Recommendation System, ALS-WR, Implicit Data
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-169711OAI: oai:DiVA.org:kth-169711DiVA: diva2:824930
External cooperation
Sportamore AB
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2015-06-30 Created: 2015-06-22 Last updated: 2015-06-30Bibliographically approved

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CiteExportLink to record
Permanent link

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