10 citations · 21 across the 6 of their papers we have counts for
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cs.IR2020
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…
cs.IR2020★ 3 cited
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…
cs.LG2020★ 10 cited
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…