@inproceedings{4a953cb3f3d642a586f12a2c41ecd055,
title = "ActRec: A Word Embedding-based Approach to Recommend Movie Actors to Match Role Descriptions",
abstract = "In this work, we propose a novel recommendation problem, actor recommendation (ActRec), based on unstructured text data for the movie industry. Given the text description of a role, we generate a ranking list of actors such that the most proper actors for the role-playing can be at top positions. We propose a word embedding-based approach to solve the ActRec problem. In addition, we compile a multi-source data from Wikipedia, Google Search, and PTT online forum. Experimental results show the promising performance of our method, which encourages future effort on ActRec.",
author = "Lee, {Ai Ni} and Chen, {Kuan Ying} and Li, {Cheng Te}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 ; Conference date: 07-12-2020 Through 10-12-2020",
year = "2020",
month = dec,
day = "7",
doi = "10.1109/ASONAM49781.2020.9381452",
language = "English",
series = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "389--392",
editor = "Martin Atzmuller and Michele Coscia and Rokia Missaoui",
booktitle = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",
address = "United States",
}