Video recommendation method based on multi-modal video content and multi-task learning
The invention discloses a video recommendation method based on multi-modal video content and multi-task learning. The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adoptin...
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| creator | SHI JINGLUN LIANG KEHONG LIN YANGCHENG FU QIANSHUAN DENG LI |
| description | The invention discloses a video recommendation method based on multi-modal video content and multi-task learning. The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adopting an attention mechanism method; learning feature representation of the social relationship of the user by adopting a deep walk method; proposing a deep neural network model based on an attention mechanism to learn multi-domain feature representation; embedding the features generated based on the above steps into a sharing layer as a multi-task model, and generating prediction results through a multi-layer perceptron. According to the method, the attention mechanism is combined with the user features to fuse the video multi-modal features, so that the whole recommendation is richer and more personalized; meanwhile, because of multi-domain features and with consideration of the importance ofinteraction features in r |
| format | Patent |
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The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adopting an attention mechanism method; learning feature representation of the social relationship of the user by adopting a deep walk method; proposing a deep neural network model based on an attention mechanism to learn multi-domain feature representation; embedding the features generated based on the above steps into a sharing layer as a multi-task model, and generating prediction results through a multi-layer perceptron. 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The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adopting an attention mechanism method; learning feature representation of the social relationship of the user by adopting a deep walk method; proposing a deep neural network model based on an attention mechanism to learn multi-domain feature representation; embedding the features generated based on the above steps into a sharing layer as a multi-task model, and generating prediction results through a multi-layer perceptron. 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The method comprises the following steps: extracting visual, audio and text features of a short video through a pre-trained model; fusing the multi-modal features of the video by adopting an attention mechanism method; learning feature representation of the social relationship of the user by adopting a deep walk method; proposing a deep neural network model based on an attention mechanism to learn multi-domain feature representation; embedding the features generated based on the above steps into a sharing layer as a multi-task model, and generating prediction results through a multi-layer perceptron. According to the method, the attention mechanism is combined with the user features to fuse the video multi-modal features, so that the whole recommendation is richer and more personalized; meanwhile, because of multi-domain features and with consideration of the importance ofinteraction features in r</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC COMMUNICATION TECHNIQUE ELECTRIC DIGITAL DATA PROCESSING ELECTRICITY PHYSICS PICTORIAL COMMUNICATION, e.g. TELEVISION |
| title | Video recommendation method based on multi-modal video content and multi-task learning |
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