@article {1336,
	title = {Artificial Intelligence (AI): new developments and innovations applied to e-commerce},
	year = {2020},
	month = {05/2020},
	institution = {European Parliament{\textquoteright}s committee on the Internal Market and Consumer Protection},
	abstract = {This in-depth analysis discusses the opportunities and challenges brought by the recent and the foreseeable developments of Artificial Intelligence into online platforms and marketplaces. The paper advocates the importance to support tustworthy, explainable AI (in order to fight discrimination and manipulation, and empower citizens), and societal-aware AI (in order to fight polarisation, monopolistic concentration and excessive inequality, and pursue diversity and openness).
This document was provided by the Policy Department for Economic, Scientific and Quality of Life Policies at the request of the committee on the Internal Market and Consumer Protection (IMCO).},
	url = {https://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_IDA(2020)648791},
	author = {Dino Pedreschi and Ioanna Miliou}
}
@article {866,
	title = {A supervised approach for intra-/inter-community interaction prediction in dynamic social networks},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {09/2016},
	pages = {86},
	abstract = {Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.},
	issn = {1869-5469},
	doi = {10.1007/s13278-016-0397-y},
	url = {http://dx.doi.org/10.1007/s13278-016-0397-y},
	author = {Giulio Rossetti and Riccardo Guidotti and Ioanna Miliou and Dino Pedreschi and Fosca Giannotti}
}
