Content recommendation method, device and system

Abstract

The invention discloses a content recommendation method, device and system. The method includes the steps: acquiring user attribute information and/or environment attribute information; combining recommendation contents based on the user attribute information and/or the environment attribute information. According to the method, more information can be provided for the recommendation contents, the use ratio of display positions of the recommendation contents is increased, and specific personal contents can be provided, so that the conversion rate of the recommendation contents is increased.

Description

Technical field [0001] This application relates to the field of computer technology, in particular to the field of terminal technology, and in particular to content recommendation methods, devices and systems. Background technique [0002] Existing content recommendation systems (such as advertisement recommendation systems, information recommendation systems, etc.) mainly recommend relevant content based on the analysis of the detected users' interests, preferences, and areas of concern. This type of content recommendation system mainly includes three modules: user detection and feature analysis module, recommendation module and display module. [0003] In the user detection and feature analysis module, after the image is collected by the camera, the target user in the image is extracted using methods such as pedestrian detection and face detection, and then feature analysis is performed on the target user. The most commonly used features in the prior art include superficial feat...

AI Technical Summary

Problems solved by technology

In practical applications, it is difficult to judge whether a product or information is suitable to be recommended to the user through these shallow features. For example, when the user is detected as a young woman, the system judges that the user may be interested in cosmetics, but cannot judge what should be recommended to the user. brand or which type of cosmetic

[0008] In the recommendation module, the preset content is saved in the content database, and the recommended preset content has the following defects: First, the recommendation module may determine multiple preset contents to be recommended, and the correlation between multiple preset contents may be poor , by displaying multiple preset content to be recommended, users will have a sense of disconnection when obtaining information; second, if the matching degree between user characteristics and the characteristics of the content to be recommended is very low, the recommendation effect will be poor; third, the current The form of recommended content and the information contained in existing technologies are fixed, and it is impossible to provide personalized recommended content to meet the needs of different users. For example, when recommending a watch, with different characters and music, the user's perception of the watch Not the same, the recommended effect is completely different

[0009] In the display module, the existing technology only serves a group of users, which is difficult to meet the individual needs of multiple users, and the amount of information in the recommended content is less

When the number of users is too large or the characteristics are too complex, the system may not be able to decide what to recommend at all

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