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Group Recommender System for Restaurant Lunches
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

A group recommender system for lunch restaurants is developed. The user interface is an

Android application which is run on a smartphone. The system features a novel approach

for implicit rating collection when a user browses a list of item descriptions. The individual

recommendation is based on extracting and comparing tf-idf feature vectors of menu texts

as well as individual rankings of the restaurants. The group recommender system works by

aggregating the individual estimated scores of the members in the group, and in addition

to this machine learning methods are used to capture the group dynamics in the group


Abstract [sv]

Inom ramen for examensarbetet utvecklas ett system for grupprekommendation av lunchrestauranger. Anvandarapplikationen laddas ner och kors pa en Android smartphone. Systemet anvander sig av en ny metod for att samla implicit information fran anvandarnas nyttjande av applikationen. Den individuella rekommendationen bygger pa att extrahera och jamfora tf-idf feature-vektorer fran menyernas text samt individuell rangordning av restaurangerna. Grupprekommendationen fas genom att aggregera de individuella estimerade betygen hos anvandarna i gruppen, och dartill anvands maskininlarningsmetoder for att simulera gruppdynamiken av gruppvalet.

Place, publisher, year, edition, pages
National Category
Computer Science
URN: urn:nbn:se:kth:diva-142393OAI: diva2:700100
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2014-03-11 Created: 2014-03-03 Last updated: 2014-03-11Bibliographically approved

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