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Comparison of user and item-based collaborative filtering on sparse data
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse av användar - och objektbaserad kollaborativ filtrering på gles data (Swedish)
Abstract [en]

Recommender systems are used extensively today in many areas to help users and consumers with making decisions. Amazon recommends books based on what you have previously viewed and purchased, Netflix presents you with shows and movies you might enjoy based on your interactions with the platform and Facebook serves personalized ads to every user based on gathered browsing information. These systems are based on shared similarities and there are several ways to develop and model them. This study compares two methods, user and item-based filtering in k nearest neighbours systems.The methods are compared on how much they deviate from the true answer when predicting user ratings of movies based on sparse data. The study showed that none of the methods could be considered objectively better than the other and that the choice of system should be based on the data set.

Abstract [sv]

Idag används rekommendationssystem extensivt inom flera områden för att hjälpa användare och konsumenter i deras val. Amazon rekommenderar böcker baserat på vad du tittat på och köpt, Netflix presenterar serier och filmer du antagligen kommer gilla baserat på interaktioner med plattformen och Facebook visar personaliserad, riktad reklam för varje enskild användare baserat på tidigare surfvanor. Dessa system är baserade på delade likheter och det finns flera sätt att utveckla och modellera dessa på. I denna rapport jämförs två metoder, användar- och objektbaserad filtrering i k nearest neighbours system. Metoderna jämförs på hur mycket de avviker från det sanna svaret när de försöker förutse användarbetyg på filmer baserat på gles data. Studien visade att man ej kan peka ut någon metod som objektivt bättre utan att val av metod bör baseras på datasetet.

Place, publisher, year, edition, pages
2017.
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-209445OAI: oai:DiVA.org:kth-209445DiVA, id: diva2:1112146
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Examiners
Available from: 2017-06-20 Created: 2017-06-20 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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Output format
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