We describe three recommender systems for on line articles that are specifically tailored for mobile devices. In order to increase the number of articles read by the average user, an on line newspaper could be personalized for each reader. Each user receives a personalized selection of the articles that take into account the limited bandwidth and screen, the user's preferences and possibly their geographical position. Two general criteria are followed: a collective intelligence criterion and a content similarity criterion. The suggested articles need to be both popular among the members of the on line community, and similar to the articles already read by the user. The three systems address three similar problems. NeoPage is a tool for newspapers' editors that suggests the position that each article should have on a web page. ARS is a tool for newspapers' readers that recommends the most similar articles to an article just read. MyNews is a tool for the readers that produces a list of recommended articles by taking into account both the popularity of the article and the previously read articles by the user.
|Titolo:||Recommendation systems for mobile devices|
|Data di pubblicazione:||2012|
|Tipologia:||2.1 Contributo in volume (Capitolo o Saggio)|