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Many amateur creators now create derivative works and put them on the web. Although there are several factors that inspire the creation of derivative works, such factors cannot usually be observed on the web. In this paper, we propose a model for inferring latent factors from sequences of derivative work posting events. We assume a sequence to be a stochastic process incorporating the following three factors: (1) the original work's attractiveness, (2) the original work's popularity, and (3) the derivative work's popularity. To characterize content popularity, we use content ranking data and incorporate rank-biased popularity based on the creators' browsing behavior. Our main contributions are three-fold: (1) to the best of our knowledge, this is the first study modeling derivative creation activity, (2) by using a real-world dataset of music-related derivative work creation to evaluate our model, we showed the effectiveness of adopting all three factors to model derivative creation activity and onsidering creators' browsing behavior, and (3) we carried out qualitative experiments and showed that our model is useful in analyzing derivative creation activity in terms of category characteristics, temporal development of factors that trigger derivative work posting events, etc.
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