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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oai:112.137.131.14:VNU_123-670922019-09-04T08:34:34Z Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction Ho, Xanh Nguyen, Nhung T.H. Advanced Technologies for IoT Applications 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 2019-09-04T08:34:34Z 2019-09-04T08:34:34Z 2017 Article Ho, X., & Nguyen, T. H. N. (2017). Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction. Advanced Technologies for IoT Applications. http://repository.vnu.edu.vn/handle/VNU_123/67092 en application/pdf |
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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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Advanced Technologies for IoT Applications |
author_facet |
Advanced Technologies for IoT Applications Ho, Xanh Nguyen, Nhung T.H. |
format |
Article |
author |
Ho, Xanh Nguyen, Nhung T.H. |
spellingShingle |
Ho, Xanh Nguyen, Nhung T.H. Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
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Ho, Xanh |
title |
Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
title_short |
Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
title_full |
Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
title_fullStr |
Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
title_full_unstemmed |
Film2Vec – A Feature-based Film Distributed Representation for Rating Prediction |
title_sort |
film2vec – a feature-based film distributed representation for rating prediction |
publishDate |
2019 |
url |
http://repository.vnu.edu.vn/handle/VNU_123/67092 |
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1680967814715801600 |