Devising News Recommendation Strategies with Process Mining Support

Abstract : News media is in a digital transformation, disrupting their existing business models. Many news media houses are looking into recommender systems as a part of their digital strategies. However, the social role of journalism, existing publishing platforms and news as a continuous data stream infer particular challenges for applying standard recommender technologies. This paper explores how news recommendation can go beyond popularity and recency and take advantage of content quality metrics and interaction patterns. This knowledge is derived through adapting process mining for usage with web logs. The proposal is evaluated on real event logs from a German news publisher, revealing encouraging results.
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download

https://hal-paris1.archives-ouvertes.fr/hal-01519729
Contributor : Elena Epure <>
Submitted on : Tuesday, May 9, 2017 - 11:07:22 AM
Last modification on : Monday, November 20, 2017 - 5:22:10 PM
Long-term archiving on : Thursday, August 10, 2017 - 12:49:30 PM

File

AISR2017_paper_17.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01519729, version 1

Collections

Citation

Elena Viorica Epure, Rebecca Deneckere, Camille Salinesi, Benjamin Kille, Jon Ingvaldsen. Devising News Recommendation Strategies with Process Mining Support. Atelier interdisciplinaire sur les systèmes de recommandation / Interdisciplinary Workshop on Recommender Systems, May 2017, Paris, France. ⟨hal-01519729⟩

Share

Metrics

Record views

226

Files downloads

219