In this paper we study the problem of scheduling a given sequence activities where each activity consumes resource with limited availability. Activity durations as well consumptions are assumed to be time-dependent. Because interaction time-dependent and consumptions, policies based on starting in early possible may fail due unnecessarily high exceeding availability resource. We propose dynamic...

This chapter shows how a system can recommend to a group of users by aggregating information from individual user models and modelling the users affective state. It summarizes results from previous research in this area. It also shows how group recommendation techniques can be applied when recommending to individuals, in particular for solving the cold-start problem and dealing with multiple cr...

We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficul...

In this paper we offer a potential solution to the cold-start problem in group recommender systems. To do so, we use information about previous group recommendation events and copy ratings from a user who played a similar role in some previous group event. We show that copying in this way, i.e. conditioned on groups, is superior to copying nothing and also superior to copying ratings from the m...

We demonstrate how fundamental properties of magnons may be manipulated using femtosecond laser pulses by performing ab initio real-time time-dependent density functional theory simulations. To illustrate this, we show the spin-wave dynamics in ${\mathrm{Fe}}_{50}{\mathrm{Ni}}_{50}$ can three different ways tailoring applied pulse to excite optically induced intersublattice transfer (OISTR): (1...