BibSLEIGH — Rafet

Travelled to:

1 × Austria

Collaborated with:

Anders Drachen C.Bauckhage C.Thurau Julian Runge C.Ojeda Fabian Hadiji K.Kersting Diego Klabjan Alessandro Canossa Nicholas Ross Y.N.Ravari P.Spronck Raheel Yawar Rajkumar Ramamurthy Sridev Srikanth Marco Tamassia William L. Raffe Fabio Zambetta M.Hitchens Eric Thurston Lundquist Yungjen Kung Pranav Simha Rao James Green Chester Gray Elie Harik Patty Lu Mike Schaekermann Giovanni Ribeiro Guenter Wallner S.Kriglstein Daniel M. Johnson 0001 L.E.Nacke Myat Aung Simon Demediuk Y.Sun Ye Tu Yu Ang Siva Nekkanti Shantanu Raghav

Talks about:

game (15) player (9) behavior (7) predict (7) destini (4) cluster (4) analysi (4) play (4) base (4) profil (3)

Person: Rafet Sifa

DBLP: Sifa:Rafet

Contributed to:

2015

2012

2013

2014

2015

2016

2017

2018

2019

2017

Wrote 20 papers:

RecSys-2015-SifaOB #analysis #migration: User Churn Migration Analysis with DEDICOM (RS, CO, CB), pp. 321–324.
CIG-2012-BauckhageKSTDC #empirical #game studies #how: How players lose interest in playing a game: An empirical study based on distributions of total playing times (CB, KK, RS, CT, AD, AC), pp. 139–146.
CIG-2012-DrachenSBT #behaviour #clustering #game studies: Guns, swords and data: Clustering of player behavior in computer games in the wild (AD, RS, CB, CT), pp. 163–170.
CIG-2013-SifaB #analysis #behaviour #game studies #learning: Archetypical motion: Supervised game behavior learning with Archetypal Analysis (RS, CB), pp. 1–8.
CIG-2013-SifaDBTC #behaviour #evolution: Behavior evolution in Tomb Raider Underworld (RS, AD, CB, CT, AC), pp. 1–8.
FDG-2013-DrachenTSB #behaviour #clustering #comparison: A comparison of methods for player clustering via behavioral telemetry (AD, CT, RS, CB), pp. 245–252.
CIG-2014-BauckhageSDTH #behaviour #clustering #game studies #heatmap #using: Beyond heatmaps: Spatio-temporal clustering using behavior-based partitioning of game levels (CB, RS, AD, CT, FH), pp. 1–8.
CIG-2014-HadijiSDTKB #predict: Predicting player churn in the wild (FH, RS, AD, CT, KK, CB), pp. 1–8.
CIG-2014-SifaBD #modelling #scalability: The Playtime Principle: Large-scale cross-games interest modeling (RS, CB, AD), pp. 1–8.
AIIDE-2015-SifaDB #analysis #behaviour #scalability: Large-Scale Cross-Game Player Behavior Analysis on Steam (RS, AD, CB), pp. 198–204.
AIIDE-2015-SifaHRDKB #game studies #mobile #predict: Predicting Purchase Decisions in Mobile Free-to-Play Games (RS, FH, JR, AD, KK, CB), pp. 79–85.
AIIDE-2016-DrachenLKRSRK #agile #game studies #mobile #predict: Rapid Prediction of Player Retention in Free-to-Play Mobile Games (AD, ETL, YK, PSR, RS, JR, DK), pp. 23–29.
CIG-2016-DrachenGGHLSK #analysis #behaviour #clustering #comparative #profiling: Guns and guardians: Comparative cluster analysis and behavioral profiling in destiny (AD, JG, CG, EH, PL, RS, DK), pp. 1–8.
CIG-2016-DrachenRRS #game studies #mobile: Stylized facts for mobile game analytics (AD, NR, JR, RS), pp. 1–8.
CIG-2016-SifaSDOB #game studies #learning #predict #representation: Predicting Retention in Sandbox Games with Tensor Factorization-based Representation Learning (RS, SS, AD, CO, CB), pp. 1–8.
CIG-2016-TamassiaRSDZH #approach #game studies #markov #modelling #online #predict: Predicting player churn in destiny: A Hidden Markov models approach to predicting player departure in a major online game (MT, WLR, RS, AD, FZ, MH), pp. 1–8.
AIIDE-2017-RavariSSD #game studies #hybrid #online #predict: Predicting Victory in a Hybrid Online Competitive Game: The Case of Destiny (YNR, PS, RS, AD), pp. 207–213.
AIIDE-2018-SifaYRB #architecture #bottom-up #comparative #evaluation #game studies #matrix #online #recommendation: Matrix and Tensor Factorization Based Game Content Recommender Systems: A Bottom-Up Architecture and a Comparative Online Evaluation (RS, RY, RR, CB), pp. 102–108.
FDG-2019-AungDSTANRKSD #game studies #profiling: The trails of Just Cause 2: spatio-temporal player profiling in open-world games (MA, SD, YS, YT, YA, SN, SR, DK, RS, AD), p. 11.
CHI-PLAY-2017-SchaekermannRWK #behaviour #game studies #metric #motivation #profiling #self: Curiously Motivated: Profiling Curiosity with Self-Reports and Behaviour Metrics in the Game “Destiny” (MS, GR, GW, SK, DMJ0, AD, RS, LEN), pp. 143–156.