You should read this! Let me explain you why! - Explaining news recommendations to users. (Contributo in atti di convegno)

Type
Label
  • You should read this! Let me explain you why! - Explaining news recommendations to users. (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/2396761.2398559 (literal)
Alternative label
  • Blanco R., Ceccarelli D., Lucchese C., Perego R., Silvestri F. (2012)
    You should read this! Let me explain you why! - Explaining news recommendations to users.
    in 21st ACM International conference on Information and knowledge management, Maui, Hawaii, 29 October - 2 November 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Blanco R., Ceccarelli D., Lucchese C., Perego R., Silvestri F. (literal)
Pagina inizio
  • 1995 (literal)
Pagina fine
  • 1999 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?id=2398559 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 5 (literal)
Note
  • PuMa (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Yahoo! Research Barcelona; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; (literal)
Titolo
  • You should read this! Let me explain you why! - Explaining news recommendations to users. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4503-1156-4 (literal)
Abstract
  • Recommender systems have become ubiquitous in content- based web applications, from news to shopping sites. None- theless, an aspect that has been largely overlooked so far in the recommender system literature is that of automati- cally building explanations for a particular recommendation. This paper focuses on the news domain, and proposes to en- hance 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 em- ploying pieces of information associated with the news. In particular, we engineer text-based, entity-based, and usage- based explanations, and make use of a Markov Logic Net- works to rank the explanations on the basis of their effec- tiveness. 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. (literal)
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