208 citations · 361 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation
Jonne Pohjankukka, Tapio Pahikkala, Paavo Nevalainen +1
In machine learning one often assumes the data are independent when evaluating model performance. However, this rarely holds in practise. Geographic information data sets are an ex…
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction
Sandor Szedmak, Anna Cichonska, Heli Julkunen +2
This paper proposes a novel method for learning highly nonlinear, multivariate functions from examples. Our method takes advantage of the property that continuous functions can be…