Process Mining for Recommender Strategies Support in News Media

Abstract : 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.
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Communication dans un congrès
IEEE Tenth International Conference on Research Challenges in Information Science (IEEE RCIS 2016), Jun 2016, Grenoble, France. 2016
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Elena Viorica Epure, Jon 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. 2016. 〈hal-01316566〉

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