TY  - CHAP
A1  - Blanco, Roi
A1  - Ceccarelli, Diego
A1  - Lucchese, Claudio
A1  - Perego, Raffaele
A1  - Silvestri, Fabrizio
N2  - 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.
CY  - New York
TI  - You should read this! let me explain you why: explaining news recommendations to users
N1  - CIKM ?12: 21st ACM Conference on Information and Knowledge Management », Maui, Hawaii, October 2012.
AV  - none
T2  -  	CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management
SP  - 1995
EP  - 1999
Y1  - 2012///
SN  - 978-1-4503-1156-4
PB  - ACM
UR  - http://doi.acm.org/10.1145/2396761.2398559
KW  - Markov logic networks
KW  -  news recommendation
KW  -  query log analysis
KW  -  recommendation snippets
ID  - eprints1456
ER  -