@article {marg2017a-fgcs,
	title = {Query personalization using social network information and collaborative filtering techniques},
	journal = {Future Generation of Computer Systems},
	volume = {Special Issue on Recommender Systems for Large-Scale Social Networks},
	year = {2017},
	month = {jan},
	pages = {440{\textendash}450},
	abstract = {Query personalization has emerged as a means to handle the issue of information volume growth, aiming to tailor query answer results to match the goals and interests of each user. Query personalization dynamically enhances queries, based on information regarding user preferences or other contextual information; typically enhancements relate to incorporation of conditions that filter out results that are deemed of low value to the user and/or ordering results so that data of high value are presented first. In the domain of personalization, social network information can prove valuable; users{\textquoteright} social networks profiles, including their interests, influence from social friends, etc. can be exploited to personalize queries. In this paper, we present a query personalization algorithm, which employs collaborative filtering techniques and takes into account influence factors between social network users, leading to personalized results that are better-targeted to the user.},
	doi = {10.1016/j.future.2017.03.015},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X17303692},
	author = {Dionisis Margaris and Costas Vassilakis and P. Georgiadis}
}
