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: Ho, Xanh, Nguyen, Nhung T.H.
Other Authors: Advanced Technologies for IoT Applications
Format: Article
Language:English
Published: 2019
Online Access:http://repository.vnu.edu.vn/handle/VNU_123/67092
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Institution: Vietnam National University, Hanoi
Language: English
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spelling 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
institution Vietnam National University, Hanoi
building VNU Library & Information Center
country Vietnam
collection VNU Digital Repository
language English
description 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
author2 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
author_sort 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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