10 citations · 21 across the 6 of their papers we have counts for
6 papers
New Recommendation Algorithm for Implicit Data Motivated by the Multivariate Normal Distribution
Markus Viljanen, Tapio Pahikkala
The goal of recommender systems is to help users find useful items from a large catalog of items by producing a list of item recommendations for every user. Data sets based on impl…
Content Based Player and Game Interaction Model for Game Recommendation in the Cold Start setting
Markus Viljanen, Jukka Vahlo, Aki Koponen +1
Game recommendation is an important application of recommender systems. Recommendations are made possible by data sets of historical player and game interactions, and sometimes the…
Generalized vec trick for fast learning of pairwise kernel models
Markus Viljanen, Antti Airola, Tapio Pahikkala
Pairwise learning corresponds to the supervised learning setting where the goal is to make predictions for pairs of objects. Prominent applications include predicting drug-target o…
Measuring Player Retention and Monetization using the Mean Cumulative Function
Markus Viljanen, Antti Airola, Anne-Maarit Majanoja +2
Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization in particular have become central busi…
Playtime Measurement with Survival Analysis
Markus Viljanen, Antti Airola, Jukka Heikkonen +1
Maximizing product use is a central goal of many businesses, which makes retention and monetization two central analytics metrics in games. Player retention may refer to various du…
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels
Tapio Pahikkala, Markus Viljanen, Antti Airola +1
We consider the problem of learning regression functions from pairwise data when there exists prior knowledge that the relation to be learned is symmetric or anti-symmetric. Such p…