Graph-based Rating Prediction using Eigenvector Centrality
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00305495" target="_blank" >RIV/68407700:21230/16:00305495 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.5220/0006044902280233" target="_blank" >http://dx.doi.org/10.5220/0006044902280233</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0006044902280233" target="_blank" >10.5220/0006044902280233</a>
Alternative languages
Result language
angličtina
Original language name
Graph-based Rating Prediction using Eigenvector Centrality
Original language description
The most of recommendation systems rely on the statistical correlations of the past explicitly given user rating for items (e.g. collaborative filtering). However, in conditions of insufficient data of past rating activities, these systems are facing difficulties in rating prediction, this situation is commonly known as the cold-start problem. This paper describes how graph-based representation and Social Network Analysis can be used to help dealing with cold-start problem. We proposed a method to predict user rating based on the hypothesis that the rating of the node in the network corresponded to the rating of the most important nodes which are connected to it. The proposed method has been particularly applied to three MovieLens datasets to evaluate rating prediction performance. Obtained results showed competitiveness of our method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
ISBN
978-989-758-203-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
228-233
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Porto
Event date
Nov 9, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000391111000023