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
Bagi pencipta amatur, telah menjadi popular untuk mencipta kandungan baharu berdasarkan karya asal sedia ada: kandungan baharu sedemikian dipanggil karya terbitan. Kita tahu bahawa penciptaan derivatif adalah popular, tetapi mengapa adakah karya terbitan individu dicipta? Walaupun terdapat beberapa faktor yang memberi inspirasi kepada penciptaan karya terbitan, faktor tersebut biasanya tidak dapat diperhatikan di Web. Dalam makalah ini, kami mencadangkan model untuk membuat kesimpulan faktor terpendam daripada urutan peristiwa siaran kerja terbitan. Kami menganggap urutan sebagai proses stokastik yang menggabungkan tiga faktor berikut: (1) daya tarikan karya asal, (2) populariti karya asal, dan (3) populariti karya terbitan. Untuk mencirikan populariti kandungan, kami menggunakan data kedudukan kandungan dan menggabungkan populariti berat sebelah pangkat berdasarkan gelagat penyemakan imbas pencipta. Sumbangan utama kami adalah tiga kali ganda. Pertama, sepanjang pengetahuan kami, ini adalah aktiviti penciptaan derivatif pemodelan kajian yang pertama. Kedua, dengan menggunakan set data dunia sebenar penciptaan karya terbitan berkaitan muzik, kami menjalankan eksperimen kuantitatif dan menunjukkan keberkesanan mengguna pakai ketiga-tiga faktor untuk memodelkan aktiviti penciptaan terbitan dan mempertimbangkan gelagat penyemakan imbas pencipta dari segi logaritma negatif kebarangkalian untuk data ujian. Ketiga, kami menjalankan eksperimen kualitatif dan menunjukkan bahawa model kami berguna dalam menganalisis aspek berikut: (1) aktiviti penciptaan terbitan dari segi ciri kategori, (2) perkembangan temporal faktor yang mencetuskan peristiwa siaran kerja terbitan, (3) ciri pencipta , (4) Proses penciptaan terbitan tertib ke-N, dan (5) kedudukan kerja asal.
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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Salinan
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
Salinan
@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},}
Salinan
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 -