Innovative Technology Developed by WiMi, The Attentional Autoenconder Network Provides Efficient Recommendation System

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced that it developed an innovative technology, attentional autoencoder network for efficient recommendation system, which takes recommendation systems to a higher level of accuracy, efficiency and user experience.

WiMi has always been committed to advancing recommendation technology, and this latest technological breakthrough will provide users with more personalized and accurate recommendation services. The new technology employs an autoencoder network and introduces an attention mechanism to address the challenges of insufficient data, cold starts and information overload that exist in traditional recommendation systems.

In previous research, recommendation systems face insufficient data and cold-start problems and challenges. With inadequate data, WiMi’s attention autoencoder network can achieve more accurate recommendations on insufficient data by learning the attribute information of users and items and automatically extracting the features that play an important role in the recommendation results.

On the cold-start problem, WiMi’s technology can personalize recommendations without sufficient historical data of the user by fusing the attribute information of the user and the item to provide a better recommendation experience for new users.

In addition to dealing with the problem of information overload, WiMi’s attentional autoencoder network combines user and item attribute information to better understand users’ interests and needs, providing a more personalised and accurate recommendation service to help users filter and access content that truly interests them.

Insufficient data problem: in recommendation systems, users rate only a small number of items, while most items have no feedback. This makes it difficult to achieve satisfactory recommendation services. This technique solves the problem by utilizing users’ attribute information to improve the accuracy and coverage of recommendations.

Cold-start problem: cold start refers to the lack of sufficient data to make accurate recommendations for new users or new programs. In a cold start situation, traditional collaborative filtering methods cannot provide effective recommendations. This technique overcomes the cold start problem by introducing the user’s attribute information, which enables personalized recommendations in the cold start situation.

Information overload problem: with the rapid development of information science, people are faced with a large amount of information, which is easy to fall into the dilemma of information overload.

Traditional recommendation systems tend to make recommendations based only on the user’s behaviour, ignoring the user’s personalised needs and preferences. This technology uses the user’s attribute information to better understand the user’s interests and needs, to provide more personalised recommendation services and alleviate the information overload problem.

The core innovation of WiMi’s development of this technology is the introduction of the attention mechanism, which enables the model to automatically learn the importance of the attribute information of users and projects, and dynamically adjust the weight of the attribute information according to different application scenarios. In this way, WiMi’s technology can adapt more flexibly to the differences between different users and projects and provide more efficient recommendation services.

WiMi’s attentional autoencoder network is a technical framework for efficient recommendation systems that combine autoencoders and attention mechanisms to improve the accuracy and efficiency of recommendations.

Its technical framework includes data preprocessing, autoencoder network, user and item feature extraction, attention mechanism, recommendation computation and evaluation, model training and optimization, hyperparameter selection and tuning.

Data preprocessing: raw data needs to be pre-processed before using the attention autoencoder network. This includes steps such as data processing, feature extraction and data normalization.

Data processing removes noise and outliers, feature extraction extracts useful attribute information from the raw data, and data normalization scales the values of different features to the same range for stability in model training and recommendation calculation.

Autoencoder networks: the core of the attentional autoencoder network is the autoencoder. An autoencoder is a neural network structure that consists of an encoder and a decoder.

The encoder converts the input data into a low-dimensional representation and the decoder reconstructs the low-dimensional representation into the input data. The goal of the autoencoder is to minimize the reconstruction error so that the reconstructed data is as similar as possible to the original data.

User and item feature extraction: the attentional autoencoder network utilizes attribute information of users and items to extract features. For users, attributes such as user’s personal information, behaviours and preferences can be used as input. For items, attributes such as categories, labels, descriptions and content features of items can be used as input.

By feeding the attribute information of users and items into the encoder part of the autoencoder network, low-dimensional representations of users and items, i.e., user characteristics and item features, can be obtained.

Attention mechanism: after obtaining user characteristics and item features, the attention mechanism is introduced to automatically learn the importance of user and item attribute information.

By giving different weights to different attribute information, the attention mechanism enables the model to focus on the attributes that are critical to the recommendation results. Attention weights can be obtained through learning or can be set based on domain knowledge. By introducing the attention mechanism, the quality and personalization of the recommendation results can be improved.

Recommendation computation and evaluation: after training, the attention autoencoder network can perform recommendation computation based on user and item features. The generated user features and item features are usually used to compute the user’s rating or probability for the item.

The recommendation results can be sorted based on the ratings or probabilities to provide the user with a personalized list of recommendations. To evaluate the effectiveness of recommendations, the quality of the recommendation results can be measured using evaluation benchmarks such as accuracy, recall, and mean average precision (MAP).

Model training and optimization: the training process of the attentional autoencoder network involves minimizing the recommendation error. Optimization algorithms such as backpropagation algorithm and gradient descent are usually used to update the weights and parameters of the model.

During the training process, the training set can be used for updating the model parameters and the validation set can be used for model tuning and selection. Through the iterative training and optimization process, the attention autoencoder network can continuously improve the accuracy and efficiency of recommendations.

Hyperparameter selection and tuning: attentional autoencoder networks also involve the selection and tuning of some hyperparameters. For example, the number of layers and nodes of the autoencoder network, the type and parameters of the attention mechanism, the learning rate and the regularization term of the optimization algorithm.

Choosing the right hyperparameters can have an important impact on the performance of the model and the recommendation results, so experiments and validation are needed to determine the optimal hyperparameter settings.

The attentional autoencoder network is a technical framework for efficient recommendation systems, which can extract features from the attribute information of users and items and perform recommendation computation based on importance weighting by combining autoencoder and attention mechanisms.

The key steps of this framework include data preprocessing, construction of an autoencoder network, user and item feature extraction, introduction of attention mechanism, recommendation computation and evaluation.

Through the training and optimization process, the attention autoencoder network can improve the accuracy, efficiency, and personalization of the recommendation system, and provide users with a better recommendation experience.

WiMi has conducted extensive experiments and evaluations of the technology and compared it with traditional recommendation methods. The experimental results show that the technology can significantly improve the quality and efficiency of recommendations and provide users with a more personalized and satisfying recommendation experience.

Attentional autoencoder networks can be applied in several different scenarios for successful real-world applications. In social networks, news, movies, and music, the technology has demonstrated excellent recommendation results. Users click-through and conversion rates are significantly improved, as is user satisfaction with the recommendation results.

In addition to significant improvements in recommendation effectiveness, WiMi’s attentional autoencoder network is highly flexible. The technology is capable of handling large-scale data and can easily adapt to the needs of recommendation systems of different sizes and domains.

Whether it is a small social network or a global e-commerce platform, the technology can efficiently provide personalized recommendation services. WiMi also plans to combine the attentional autoencoder network with other advanced recommendation technologies to further enhance recommendation effectiveness.

For example, the combination of deep reinforcement learning technology will enable the recommendation system to continuously optimize the recommendations based on user feedback, providing more personalized and accurate recommendations.