ActRec: A Word Embedding-based Approach to Recommend Movie Actors to Match Role Descriptions

Ai Ni Lee, Kuan Ying Chen, Cheng Te Li

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
編輯Martin Atzmuller, Michele Coscia, Rokia Missaoui
發行者Institute of Electrical and Electronics Engineers Inc.
頁面389-392
頁數4
ISBN(電子)9781728110561
DOIs
出版狀態Published - 2020 十二月 7
事件12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands
持續時間: 2020 十二月 72020 十二月 10

出版系列

名字Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Conference

Conference12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
國家/地區Netherlands
城市Virtual, Online
期間20-12-0720-12-10

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦網路與通信
  • 資訊系統與管理
  • 社會心理學
  • 通訊

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