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Improving search results with machine learning: Classifying multi-source data with supervised machine learning to improve search results
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Sony’s Support Application team wanted an experiment to be conducted by which they could determine if it was suitable to use Machine Learning to improve the quantity and quality of search results of the in-application search tool. By improving the quantity and quality of the results the team wanted to improve the customer’s journey. A supervised machine learning model was created to classify articles into four categories; Wi-Fi & Connectivity, Apps & Settings, System & Performance, andBattery Power & Charging. The same model was used to create a service that categorized the search terms into one of the four categories. The classified articles and the classified search terms were used to complement the existing search tool. The baseline for the experiment was the result of the search tool without classification. The results of the experiment show that the number of articles did indeed increase but due mainly to the broadness of the categories the search results held low quality.

Place, publisher, year, edition, pages
2018. , p. 33
Keywords [en]
Searcher Frustration, Information Retrieval, Search Results, Topic Classification, Machine Learning, Supervised Classification, Naive Bayes
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:lnu:diva-75598OAI: oai:DiVA.org:lnu-75598DiVA, id: diva2:1216559
External cooperation
Sony Mobile Communications Inc.
Subject / course
Computer Science
Educational program
Datavetenskap, kandidatprogram, 60 hp
Presentation
2018-05-29, 09:40 (English)
Examiners
Available from: 2018-06-12 Created: 2018-06-11 Last updated: 2018-06-12Bibliographically approved

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fulltext(625 kB)1 downloads
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d9aeb7ebf0170c46b0407fee2c13b417153047beba3372dd7b0ccff9579d0ee93f0bd3151b474c3b0e7de02f262e4dca7a03fb6e44151d7000119ea35e0d3f90
Type fulltextMimetype application/pdf

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Department of computer science and media technology (CM)
Computer Sciences

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CiteExportLink to record
Permanent link

Direct link
Cite
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
  • html
  • text
  • asciidoc
  • rtf