Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction
Approaches for film recommendation systems usually exploit explicit descriptive features to compute ratings. In this paper, we suggest a different approach – to rate films via their related neighbors computed via distributed representation of movies. Specifically, we present Film2Vec, a distributed...
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| Main Authors: | , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2019
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| 在線閱讀: | http://repository.vnu.edu.vn/handle/VNU_123/67092 |
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| 機構: | Vietnam National University, Hanoi |
| 語言: | English |
| 總結: | Approaches for film recommendation systems usually exploit explicit descriptive features to compute ratings. In this paper, we suggest a different approach – to rate films via their related neighbors computed via distributed representation of movies. Specifically, we present Film2Vec, a distributed representation learning for films adapted from the distributed hypothesis from linguistics. We implement our proposed idea using TensorFlow , a Google’s Deep Neural Networks software. The experimental results on Movielens dataset show that Film2Vec can effectively reduce root mean square error (RMSE) in movie recommendation task, suggesting yet another beneficial application of deep learning |
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