%0 Book Section
%A Blanco, Roi
%A Ceccarelli, Diego
%A Lucchese, Claudio
%A Perego, Raffaele
%A Silvestri, Fabrizio
%B  	CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management
%C New York
%D 2012
%F eprints:1456
%I ACM
%K Markov logic networks, news recommendation, query log analysis, recommendation snippets
%P 1995-1999
%T You should read this! let me explain you why: explaining news recommendations to users
%U http://eprints.imtlucca.it/1456/
%X Recommender systems have become ubiquitous in contentbased  web applications, from news to shopping sites. Nonetheless,  an aspect that has been largely overlooked so far in the recommender system literature is that of automatically  building explanations for a particular recommendation.  This paper focuses on the news domain, and proposes to enhance effectiveness of news recommender systems by adding,  to each recommendation, an explanatory statement to help  the user to better understand if, and why, the item can be  her interest. We consider the news recommender system as a  black-box, and generate different types of explanations employing pieces of information associated with the news. In  particular, we engineer text-based, entity-based, and usagebased explanations, and make use of a Markov Logic Networks to rank the explanations on the basis of their effectiveness. The assessment of the model is conducted via a user study on a dataset of news read consecutively by actual users. Experiments show that news recommender systems  can greatly benefit from our explanation module.
%Z CIKM ’12: 21st ACM Conference on Information and Knowledge Management », Maui, Hawaii, October 2012.