Archetypal Game Recommender Systems

Rafet Sifa, C. Bauckhage, Anders Drachen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

23 Citationer (Scopus)

Abstract

Contemporary users (players, consumers) of digital games
have thousands of products to choose from, which makes nding games
that t their interests challenging. Towards addressing this challenge, in
this paper two dierent formulations of Archetypal Analysis for Top-L
recommender tasks using implicit feedback are presented: factor- and
neighborhood-oriented models. These form the rst application of rec-
ommender systems to digital games. Both models are tested on a dataset
of 500,000 users of the game distribution platform Steam, covering game
ownership and playtime data across more than 3000 games. Compared to
four other recommender models (nearest neighbor, two popularity mod-
els, random baseline), the archetype based models provide the highest
recall rates showing that Archetypal Analysis can be successfully applied
for Top-L recommendation purposes
OriginalsprogEngelsk
TitelProceedings of the 16th LWA Workshops: KDML, IR and FGWM
RedaktørerThomas Seidl, Marwan Hassani, Christian Beecks
ForlagCEUR Workshop Proceedings
Publikationsdato2014
Sider45-56
StatusUdgivet - 2014
BegivenhedLearning, Knowledge, Adaption (LMA) Conference - Aachen, Tyskland
Varighed: 8 sep. 201410 sep. 2014

Konference

KonferenceLearning, Knowledge, Adaption (LMA) Conference
Land/OmrådeTyskland
ByAachen
Periode08/09/201410/09/2014
NavnCEUR Workshop Proceedings
Vol/bind1226
ISSN1613-0073

Citationsformater