WiMi Hologram Cloud Inc., a leading global Hologram Augmented Reality Technology provider, announced that it developed a deep learning-based multi-modal video recommendation system. This emerging technology uses advanced algorithms and multi-modal data analysis to provide users with personalized video recommendation services, enabling a whole new world of movie watching for users.
The core of WiMi’s recommendation system is a deep learning algorithm, which is capable of extracting rich hidden features from video data and generating accurate recommendations based on the user’s personal preferences. Among them, feature extraction is the key step of the whole system. Currently, the technology adopts a convolutional neural network (CNN) as the main algorithm for feature extraction. CNN is a deep learning model based on neural networks with excellent image processing and feature extraction capabilities. In the multi-modal video recommendation system, we use CNN to dig out the hidden features of users and videos from video footage datasets. The algorithm contains three main parts: convolutional layer, pooling layer and fully connected layer.
The convolutional layer is the core of CNN that recognizes and extracts various features from the input data. Through multiple convolutional operations, it can capture contextual features from video footage data, including the type of video, title, cover, etc. The extraction of these features allows the system to better understand the video content and user preferences.
The pooling layer plays the role of compression and screening in the feature extraction process. It is able to select representative local features and compress the data into a more compact representation. Through the operation of the pooling layer, the system is able to process large-scale video data more efficiently and understand the user’s interests better.
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The fully connected layer is the final layer of a CNN. The fully connected layer is the last layer in the CNN. With the operation of the fully connected layer, the system is able to combine the user’s personalized information with the features of the video to calculate the user’s potential interest and preferences for the video.
To implement this algorithm, WiMi slightly changed the the CNN structure. This model consists of four key components: an input layer, a convolutional layer, a pooling layer, and an output layer.
In a video recommendation system, the input layer plays the role of converting the raw data into a digital matrix. This matrix represents the data required for the next convolutional operation. Then, the contextual features of the input data are extracted from the video footage dataset through three convolutional layers. These convolutional layers are designed to have different dimensions to better capture the diversity of the video content.
Next comes the pooling layer, whose task is to compress and filter the features extracted from the convolutional layer. By selecting the most representative local features, the pooling layer is able to reduce the dimensionality of the data and retain the most important information. This has the advantage of reducing the computational complexity of the system while improving the understanding of the user’s interests.
Finally, there is the output layer which generates the final recommendation results. The potential user preferences for the videos are calculated through the full-connected layer. Based on the results, the system can generate the top few recommended videos for the user to choose to watch.
In practical applications, four key parameters of the video (video ID, type, title, and cover) and four key parameters of the user (user ID, gender, age, and occupation) are generally selected as input data. These parameters provide basic information about the user and the video, generating an initial matrix for the subsequent feature extraction process. By continuously optimizing and training the model, the system is able to understand the user’s preferences more accurately and recommend the most appropriate video content for them.
The algorithmic architecture of WiMi‘s deep learning-based multi-modal video recommendation system offers a number of advantages to users. First, with the feature extraction capability of CNN, the system is able to accurately capture the hidden features of the video and the user, thus providing more accurate personalized recommendations. Second, the operation of the pooling layer reduces the dimensionality of the data and improves the computational efficiency of the system. Most importantly, through continuous training and optimization, the system is able to continuously learn and adapt to the user’s changing interests to provide better recommendation results. Deep learning-based multi-modal video recommendation systems are leading personalized recommendation technology into a new era. With the growth of data volume and the continuous progress of algorithms, the technology can better meet the needs of users and promote the progress of personalized recommendation technology.
SOURCE: PRNewswire
