The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
아마추어 창작자들에게는 기존의 원본 저작물을 기반으로 새로운 콘텐츠를 제작하는 것이 인기를 얻고 있습니다. 이러한 새로운 콘텐츠를 2차 저작물이라고 합니다. 파생 창작이 인기가 많다는 것은 알고 있지만 why 개별적인 파생 저작물이 만들어지나요? 1차 저작물 창작에 영감을 주는 몇 가지 요소가 있지만 일반적으로 웹에서는 이러한 요소를 관찰할 수 없습니다. 본 논문에서는 일련의 파생 작업 게시 이벤트로부터 잠재 요인을 추론하기 위한 모델을 제안합니다. 우리는 시퀀스가 다음 세 가지 요소, 즉 (2) 원작의 매력, (3) 원작의 인기, (1) 파생 저작물의 인기를 통합하는 확률론적 프로세스라고 가정합니다. 콘텐츠 인기를 특성화하기 위해 당사는 콘텐츠 순위 데이터를 사용하고 제작자의 탐색 행동을 기반으로 순위 편향 인기를 통합합니다. 우리의 주요 기여는 세 가지입니다. 첫째, 우리가 아는 한, 이는 파생상품 창작 활동을 모델링한 최초의 연구입니다. 둘째, 음악 관련 파생 저작물의 실제 데이터 세트를 사용하여 정량적 실험을 수행하고 파생 창작 활동을 모델링하기 위해 세 가지 요소를 모두 채택하고 창작자의 탐색 행동을 음의 로그 측면에서 고려하는 효과를 보여주었습니다. 테스트 데이터. 셋째, 질적 실험을 수행하여 우리 모델이 (2) 카테고리 특성 측면의 파생물 창작 활동, (3) 파생 저작물 게시 이벤트를 유발하는 요인의 시간적 발전, (4) 창작자 특성을 분석하는 데 유용하다는 것을 보여주었습니다. , (5) N차 파생물 생성 과정, (XNUMX) 원저작물 순위.
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)
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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
부
@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},}
부
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 -