Process Mining for Recommender Strategies Support in News Media

Elena Viorica Epure (1) , Jon Espen Ingvaldsen (2) , Rebecca Deneckere (1) , Camille Salinesi (1)

1 CRI - Centre de Recherche en Informatique de Paris 1
2 NTNU - Norwegian University of Science and Technology [Trondheim]

The strategic transition of media organizations to personalized information delivery has urged the need for richer methods to analyze the customers. Though useful in supporting the creation of recommender strategies, the current data mining techniques create complex models requiring often an understanding of techniques in order to interpret the results. This situation together with the recommender technologies deluge and the particularities of the news industry pose challenges to the news organization in making decisions about the most suitable strategy. Therefore, we propose process mining as a high-level, end-to-end solution to provide insights into the consumers' behavior and content dynamics. Specifically, we explore if it allows news organizations to analyze independently and effectively their data in order to support them in defining recommender strategies. The solution was implemented in a case study with the third largest news provider in Norway and yielded preliminary positive results. To our knowledge, this is the first attempt to apply a process mining methodology and adapt the techniques to support media industry with the recommender strategies.

Keywords: intention mining; process mining; intentional process models; behavioral analysis; recommender strategies; process mining methodology; news media case study

Domains: Computer Science [cs]; Technology for Human Learning

Elena Viorica Epure, Jon Espen Ingvaldsen, Rebecca Deneckere, Camille Salinesi. Process Mining for Recommender Strategies Support in News Media. IEEE Tenth International Conference on Research Challenges in Information Science (IEEE RCIS 2016), Jun 2016, Grenoble, France. ⟨hal-01316566⟩