Abstract
We investigate the problem of early prediction of item popularity in online social networks. Prior work claims that the time taken by each item to reach i adopters (i being a small number around 5) has a higher predictive power than other non-temporal features, such as those related to the characteristics of the adopters. Here, we challenge this claim by proposing a new feature, based on the users’ intuitions, which is shown to provide significantly better predictive power for the most popular items than the above-mentioned temporal feature. A GoodReads dataset is used to illustrate the merits of the proposed method.
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Notes
- 1.
This is a two-class classification problem.
- 2.
i.e. long after the item was released.
References
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Sbihi, N., Gryech, I., Ghogho, M. (2017). Leveraging User Intuition to Predict Item Popularity in Social Networks. In: Sabir, E., García Armada, A., Ghogho, M., Debbah, M. (eds) Ubiquitous Networking. UNet 2017. Lecture Notes in Computer Science(), vol 10542. Springer, Cham. https://doi.org/10.1007/978-3-319-68179-5_5
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DOI: https://doi.org/10.1007/978-3-319-68179-5_5
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