Graph-based Rating Prediction using Eigenvector Centrality

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Graph-based Rating Prediction using Eigenvector Centrality

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Graph-based Rating Prediction using Eigenvector Centrality

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

228-233

Název nakladatele

SciTePress - Science and Technology Publications

Místo vydání

Porto

Místo konání akce

Porto

Datum konání akce

9. 11. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391111000023