Computing device and method of operation thereof

By utilizing category-specific content consumption information from computing devices, heterogeneous content similarity can be directly extracted from the user's multi-device consumption history. This solves the problems of data quality dependence and computational complexity in heterogeneous content recommendation in existing technologies, and achieves fast and accurate recommendation results.

CN122139202APending Publication Date: 2026-06-02SAMSUNG ELECTRONICS CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SAMSUNG ELECTRONICS CO LTD
Filing Date
2024-10-14
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

Existing collaborative filtering recommendation systems suffer from problems such as high dependence on data quality, high computational complexity, need for active human intervention, and low performance when data is insufficient in heterogeneous content recommendation.

Method used

By computing device-specific content consumption information based on categories, user-specific heterogeneous content consumption information is obtained. Heterogeneous content is recommended using a similarity matrix, avoiding reliance on complex neural networks and directly extracting similarity from the user's multi-device consumption history.

Benefits of technology

It enables fast and accurate content recommendation in heterogeneous content recommendation, reduces computational complexity and dependence on metadata, and improves the autonomy and efficiency of recommendation.

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Abstract

A computing device according to the disclosure includes a memory storing at least one instruction and at least one processor configured to execute the at least one instruction stored in the memory, wherein the at least one processor can obtain a similarity between heterogeneous contents based on user-specific heterogeneous content consumption information obtained from category-specific content consumption information, and can determine a second content item of a second category different from a first category as a recommended content for a user who has consumed a first content item of the first category based on the similarity between the heterogeneous contents.
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