18416924. RECOMMENDATION METHOD, METHOD FOR TRAINING RECOMMENDATION MODEL, AND RELATED PRODUCT simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)
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RECOMMENDATION METHOD, METHOD FOR TRAINING RECOMMENDATION MODEL, AND RELATED PRODUCT
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RECOMMENDATION METHOD, METHOD FOR TRAINING RECOMMENDATION MODEL, AND RELATED PRODUCT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18416924 titled 'RECOMMENDATION METHOD, METHOD FOR TRAINING RECOMMENDATION MODEL, AND RELATED PRODUCT
The abstract describes a recommendation device that uses deep neural networks to provide recommendations based on similarities between user and item features in reference samples.
- The recommendation device analyzes user and item feature data in reference samples and to-be-predicted data.
- It identifies partially identical user and item feature data in the reference samples and to-be-predicted data.
- Target feature information is obtained based on the reference samples and to-be-predicted data.
- The target feature information is used as input to a deep neural network to generate recommendations.
Potential Applications: - Personalized product recommendations in e-commerce platforms. - Content recommendations on streaming services. - Job recommendations on career websites.
Problems Solved: - Enhances user experience by providing tailored recommendations. - Increases user engagement and satisfaction. - Helps users discover new items or content of interest.
Benefits: - Improved user satisfaction and retention. - Increased sales and conversions for businesses. - Enhanced relevance of recommendations leading to better user engagement.
Commercial Applications: Title: "Enhanced Recommendation System for Personalized User Experiences" This technology can be utilized in various industries such as e-commerce, entertainment, and recruitment to provide personalized recommendations, leading to increased user engagement and revenue generation.
Questions about the technology: 1. How does the recommendation device determine the similarities between user and item features? 2. What are the key advantages of using deep neural networks for generating recommendations?
Original Abstract Submitted
A recommendation device obtains to-be-predicted data and a plurality of target reference samples based on a similarity between the to-be-predicted data and the plurality of reference samples. Each reference sample and the to-be-predicted data each include user feature field data indicating a feature of a target user, and item feature field data indicating a feature of a target item. Each target reference sample and the to-be-predicted data have partially identical user feature field data and/or item feature field data. The recommendation device obtains target feature information of the to-be-predicted data based on the plurality of target reference samples and the to-be-predicted data. The recommendation device then uses the target feature information as input to a deep neural network to obtain a target item that is to be recommended.
