19 citations · 35 across the 9 of their papers we have counts for
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cs.IR2016
Exploiting sparsity to build efficient kernel based collaborative filtering for top-N item recommendation
Mirko Polato, Fabio Aiolli
The increasing availability of implicit feedback datasets has raised the interest in developing effective collaborative filtering techniques able to deal asymmetrically with unambi…
cs.IR2016
Boolean kernels for collaborative filtering in top-N item recommendation
Mirko Polato, Fabio Aiolli
In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Cl…
cs.AI2016★ 12 cited
Time and Activity Sequence Prediction of Business Process Instances
Mirko Polato, Alessandro Sperduti, Andrea Burattin +1
The ability to know in advance the trend of running process instances, with respect to different features, such as the expected completion time, would allow business managers to ti…