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Metrical Representation of Readers and Articles in a Digital Newspaper

Author:
Díez Peláez, JorgeUniovi authority; Martínez Rego, David; Alonso-Betanzos, Amparo; Luaces Rodríguez, ÓscarUniovi authority; Bahamonde Rionda, AntonioUniovi authority
Publication date:
2016
Editorial:

ACM

Publisher version:
http://dx.doi.org/10.1145/2959100.2959202
Abstract:

Personalized recommendation of news in digital journals have to deal with important peculiarities. A majority of users (readers) are anonymous, and frequently news are volatile, they have an extremely short duration while other items arise. In this paper, we learn a mapping of users and items into a common Euclidean space where the similarities can be computed in a linear geometric context. The location of readers in the map are re ned as they read more articles, and at the same time news can be inserted or removed eas- ily. The metric properties of readers and news will pave the way for a solid base to o er recommendations for readers not only adjusted to their tastes, but with a certain degree of di- versity or serendipity. Additionally, clusters of readers with similar interests or tastes could be discovered and exploited for marketing purposes. This mapping is learned using a scalable factorization algorithm that aims at optimizing the accuracy of the personalized recommendations. The paper includes an experimental study done with real word data

Personalized recommendation of news in digital journals have to deal with important peculiarities. A majority of users (readers) are anonymous, and frequently news are volatile, they have an extremely short duration while other items arise. In this paper, we learn a mapping of users and items into a common Euclidean space where the similarities can be computed in a linear geometric context. The location of readers in the map are re ned as they read more articles, and at the same time news can be inserted or removed eas- ily. The metric properties of readers and news will pave the way for a solid base to o er recommendations for readers not only adjusted to their tastes, but with a certain degree of di- versity or serendipity. Additionally, clusters of readers with similar interests or tastes could be discovered and exploited for marketing purposes. This mapping is learned using a scalable factorization algorithm that aims at optimizing the accuracy of the personalized recommendations. The paper includes an experimental study done with real word data

Description:

10th ACM Conference on Recommender Systems (RecSys 2016), Boston, MA, USA, 15th-19th September. RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations

URI:
http://hdl.handle.net/10651/39367
DOI:
10.1145/2959100.2959202
Patrocinado por:

This work was funded by grants TIN2015-65069-C2-1-R and TIN2015-65069-C2-2-R fromMinisterio de Econom a y Com- petitividad. DavidMart nez-Rego acknowledges support from the Xunta de Galicia under postdoctoral grant code POS- A/2013/196

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