A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks

Manqing Ma, Wei Pang, Lan Huang, Zhe Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We are interested in the relationship between the team composition and the outcome in the filmmaking process. We studied the “diversity” of the group of actors and directors and how it is related to the movie rank given by the audience. The “diversity” is considered as the representation of the degree of variety based on the possibilities of collaborations among its actors and directors. Their collaboration network for the movie was first generated from the “background” network of the collaborations from other works. Then a shortest-path method together with the Adamic/Adar method are used to form indirect links. Finally the “complete” collaboration network can be generated and the “diversity” measures are thus defined accordingly. We experimented on the France and Germany datasets and identified consistent patterns: the lower the “diversity” is, the lower the movie rank will be. We also demonstrated that a subset of our diversity measures were effective in the binary classification task for movie ranks, while the advantages are prone to Precision/Recall depending on the specific dataset. This further shows that the “diversity” measure is feasible and effective in distinguishing movie ranks.
Original languageEnglish
Title of host publicationPAKDD 2017: Advances in Knowledge Discovery and Data Mining
EditorsJinho Kim, Kyuseok Shim, Longbing Cao, Jae-Gil Lee, Xuemin Lin, Yang-Sae Moon
PublisherSpringer
Pages750-761
Number of pages12
ISBN (Electronic)978-3-319-57454-7
ISBN (Print)978-3-319-57453-0
DOIs
Publication statusPublished - 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining 2017 - Jeju, Korea, Democratic People's Republic of
Duration: 23 May 201726 May 2017

Publication series

NameLecture Notes in Computer Science
Volume10234

Conference

Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining 2017
Abbreviated titlePAKDD 2017
CountryKorea, Democratic People's Republic of
CityJeju
Period23/05/1726/05/17

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    Ma, M., Pang, W., Huang, L., & Wang, Z. (2017). A Novel Diversity Measure for Understanding Movie Ranks in Movie Collaboration Networks. In J. Kim, K. Shim, L. Cao, J-G. Lee, X. Lin, & Y-S. Moon (Eds.), PAKDD 2017: Advances in Knowledge Discovery and Data Mining (pp. 750-761). (Lecture Notes in Computer Science; Vol. 10234). Springer. https://doi.org/10.1007/978-3-319-57454-7_58