WiMi Announced a Multi-View Hybrid Recommendation Model Based on Deep Learning

Rhea-AI Summary

WiMi Hologram Cloud Inc. has developed a deep learning-based multi-view hybrid recommendation model that can provide more accurate and personalized recommendation results. The model synthesizes information from different views to gain a comprehensive understanding of user interests and preferences. By combining data from user behavior, social networks, and content, the model improves the accuracy and personalization of the recommendation system. WiMi's model utilizes deep learning techniques to construct a comprehensive recommendation approach that adapts to different data types and adjusts the recommendation strategy.

BEIJING, Nov. 2, 2023 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that a deep learning-based multi-view hybrid recommendation model was developed. This model can consider multiple views and information sources simultaneously to capture the relationship between users and items more comprehensively. By fusing features from different views, the multi-view hybrid recommendation model can provide more accurate and personalized recommendation results.

The significance of the multi-view hybrid recommendation model lies in the ability to synthesize information from different views to gain a more comprehensive understanding of the user's interests and preferences. For example, user behavior data can reflect the user's historical behavior and preferences, social network data can reflect the user's social relationships and social influence, and content data can reflect the attributes and characteristics of items. By combining information from these different views, we can more accurately predict users' interests, improve the accuracy and personalization of the recommendation system, and at the same time solve the data sparsity and cold-start problems so as to provide better recommendation results.

Deep learning is utilized in WiMi's deep learning-based multi-view hybrid recommendation system for feature learning and recommendation model construction. Feature learning refers to the automatic learning of user and item representations through deep neural networks, so that user interests and item characteristics can be better captured. Recommendation model construction, on the other hand, refers to applying the learned features to specific recommendation tasks, such as user behavior-based recommendation, content-based recommendation, and so on. Commonly used models for the application of deep learning in recommendation systems include matrix decomposition-based models, convolutional neural network-based models, recurrent neural network-based models, and so on. These models make recommendations by learning user and item representations and combining user behavior and item features.

In a multi-view hybrid recommendation model, we need to consider information from multiple views (e.g., user behavior, item attributes, social networks, etc.) to make recommendations. The details of the deep learning-based multi-view hybrid recommendation model developed by WiMi are as follows:

Input layer: Firstly, the feature representation of each view is used as the input to the model, for each view, we use different feature extraction methods, for example, for the user behavior view, we can use the user's click record as the feature; for the item attribute view, we can use the item's attribute vector as the feature.

View feature integration layer: In this layer, we integrate the features of different views, and can use some integration methods to fuse the information of different views together to get a more comprehensive feature representation.

Feature encoding layer: The integrated features are encoded using deep learning models (e.g., neural networks). This maps the high-dimensional features to a low-dimensional representation space and extracts more useful features.

Feature interaction layer: The encoded features interact with each other, and some interaction methods can be used, such as dot product and weighted summation, so that the interactions between different features can be captured and the expressive power of the model can be improved.

Output layer: Using some output layer methods such as a fully connected layer, softmax, etc., the features are mapped to a probability distribution of the recommendation results, so that a recommendation result can be obtained for each user.

With the above model layers, the information from multiple views can be fully utilized to improve the accuracy and personalization of recommendations. At the same time, the model is also highly scalable and it is easy to add new views or adjust the model structure.

With the popularization of the Internet and mobile Internet, the application scenarios of recommendation systems are becoming more and more extensive, such as e-commerce, news reading, music recommendation and so on. At the same time, with the continuous development of data collection and storage technology, the available data types and quantities are also increasing, which provides a broader space for the development of recommendation systems.

The WiMi's deep learning-based multi-view hybrid recommendation model utilizes deep learning techniques to combine information from multiple views or perspectives to construct a comprehensive recommendation model approach, which can comprehensively utilize multiple types of data to provide more accurate and personalized recommendation services, and can also adaptively adjust the recommendation strategy so as to improve the recommendation effect and user satisfaction.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will," "expects," "anticipates," "future," "intends," "plans," "believes," "estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

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SOURCE WiMi Hologram Cloud Inc.

FAQ

What did WiMi Hologram Cloud Inc. announce?

WiMi announced the development of a deep learning-based multi-view hybrid recommendation model.

What is the significance of the multi-view hybrid recommendation model?

The model can synthesize information from different views to gain a comprehensive understanding of user interests and preferences, improving the accuracy and personalization of the recommendation system.

How does WiMi's model utilize deep learning?

WiMi's model utilizes deep learning for feature learning and recommendation model construction. It uses deep neural networks to automatically learn user and item representations and combines user behavior and item features for recommendations.

What are the layers of WiMi's multi-view hybrid recommendation model?

The model consists of an input layer, view feature integration layer, feature encoding layer, feature interaction layer, and output layer.

What types of data can the model utilize?

The model can utilize data from multiple views, including user behavior, item attributes, social networks, etc.

What are the potential applications of recommendation systems?

Recommendation systems have extensive applications in e-commerce, news reading, music recommendation, and more.

How does WiMi's model improve recommendation services?

WiMi's model combines information from multiple views to provide more accurate and personalized recommendation services. It can adaptively adjust the recommendation strategy to improve the recommendation effect and user satisfaction.