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Comparing ALS And SGD Over A Variable Number Of Latent Features Using Graphlab Create’s Factorization Machine
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Jämförelse Mellan ALS Och SGD Med Avseende På Antalet Latentfaktorer Genom Graphlab Create’s Factorization Machine (Swedish)
Abstract [en]

Recommendation Systems applies Information retrieval to filter necessary information from unnecessary information. Collaborative Filtering (CF) is a widely used technique to build Recommendation Engines. There are major challenges, which includes both data sparsity and growing data, for todays CF algorithms. An attempt on solving these challenges involves using Matrix Factorization (MF). In this paper we attempt to present a comparison between two highly used and well known MF algorithms called ALS and SGD, which can be served as a roadmap for future research.

Abstract [sv]

Rekommendationssystem använder sig av informationssökning för att filtrera nödvändig information från onödig information. Samarbetesfiltrering (SF) är en välanvänd teknik för att bygga Rekommendations- system. Det finns stora utmaningar gällande data- densitet och den faktiska mängden data för dagens SF-algoritmer. I ett försök att lösa dessa utmaningar ligger användningen av Matris Faktorisering (MF). I denna rapport presenterar vi en jämförelse mellan två välkända MF-algoritmer som kallas ALS och SGD. Denna jämförelse kan komma att användas som underlag för framtida forskning. 

Place, publisher, year, edition, pages
2016. , 61 p.
National Category
Computer Science
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URN: urn:nbn:se:kth:diva-187239OAI: oai:DiVA.org:kth-187239DiVA: diva2:929350
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Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2016-05-18Bibliographically approved

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CiteExportLink to record
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  • apa
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