Abstract
How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.
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Keywords
- Root Mean Square Error
- Minimum Description Length
- Average Root Mean Square Error
- Minimum Description Length Principle
- Content Popularity
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Figueiredo, F., Almeida, J.M., Matsubara, Y., Ribeiro, B., Faloutsos, C. (2014). Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2014. Lecture Notes in Computer Science(), vol 8724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44848-9_25
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DOI: https://doi.org/10.1007/978-3-662-44848-9_25
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