Fusion of auto encoders and multi-modal data based video recommendation method
Nowadays, the commonly used linear structure video recommendation methods have the problems of non-personalized recommendation results and low accuracy, so it is extremely urgent to develop high-precision personalized video recommendation method.A video recommendation method based on the fusion of a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Beijing Xintong Media Co., Ltd
2021-02-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2021031/ |
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| Summary: | Nowadays, the commonly used linear structure video recommendation methods have the problems of non-personalized recommendation results and low accuracy, so it is extremely urgent to develop high-precision personalized video recommendation method.A video recommendation method based on the fusion of autoencoders and multi-modal data was presented.This method fused two data including text and vision for video recommendation.To be specific, the method proposed firstly used bag of words and TF-IDF methods to describe text data, and then fused the obtained features with deep convolutional descriptors extracted from visual data, so that each video document could get a multi-modal descriptors, and constructed low-dimensional sparse representation by autoencoders.Experiments were performed on the proposed model by using three real data sets.The result shows that compared with the single-modal recommendation method, the recommendation results of the proposed method are significantly improved, and the performance is better than the reference method. |
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| ISSN: | 1000-0801 |