IEICE TRANSACTIONS on Information
Modeling N-th Order Derivative Creation Based on Content Attractiveness and Time-Dependent Popularity
Summary :
For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? 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 behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.5 pp.969-981
- Publication Date
- 2020/05/01
- Publicized
- 2020/02/05
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019DAP0008
- Type of Manuscript
- Special Section PAPER (Special Section on Data Engineering and Information Management)
- Category
Authors
Kosetsu TSUKUDA
National Institute of Advanced Industrial Science and Technology (AIST)
Masahiro HAMASAKI
National Institute of Advanced Industrial Science and Technology (AIST)
Masataka GOTO
National Institute of Advanced Industrial Science and Technology (AIST)
Keyword
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Kosetsu TSUKUDA, Masahiro HAMASAKI, Masataka GOTO, "Modeling N-th Order Derivative Creation Based on Content Attractiveness and Time-Dependent Popularity" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 5, pp. 969-981, May 2020, doi: 10.1587/transinf.2019DAP0008.
Abstract: For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? 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 behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019DAP0008/_p
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@ARTICLE{e103-d_5_969,
author={Kosetsu TSUKUDA, Masahiro HAMASAKI, Masataka GOTO, },
journal={IEICE TRANSACTIONS on Information},
title={Modeling N-th Order Derivative Creation Based on Content Attractiveness and Time-Dependent Popularity},
year={2020},
volume={E103-D},
number={5},
pages={969-981},
abstract={For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? 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 behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.},
keywords={},
doi={10.1587/transinf.2019DAP0008},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - Modeling N-th Order Derivative Creation Based on Content Attractiveness and Time-Dependent Popularity
T2 - IEICE TRANSACTIONS on Information
SP - 969
EP - 981
AU - Kosetsu TSUKUDA
AU - Masahiro HAMASAKI
AU - Masataka GOTO
PY - 2020
DO - 10.1587/transinf.2019DAP0008
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2020
AB - For amateur creators, it has been becoming popular to create new content based on existing original work: such new content is called derivative work. We know that derivative creation is popular, but why are individual derivative works created? 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 behaviors. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling derivative creation activity. Second, by using real-world datasets of music-related derivative work creation, we conducted quantitative experiments and showed the effectiveness of adopting all three factors to model derivative creation activity and considering creators' browsing behaviors in terms of the negative logarithm of the likelihood for test data. Third, we carried out qualitative experiments and showed that our model is useful in analyzing following aspects: (1) derivative creation activity in terms of category characteristics, (2) temporal development of factors that trigger derivative work posting events, (3) creator characteristics, (4) N-th order derivative creation process, and (5) original work ranking.
ER -
English
