Personalized program recommendation method and device based on answer mode

Abstract

The invention relates to a personalized program recommendation method and device based on an answer mode, and the method comprises the steps: obtaining the historical viewing behavior data of a user; analyzing historical viewing behavior data of the users, and calculating portraits of the users; when the user uses the television, calling and starting the answering client, providing a plurality of questions for the user so that the user can answer the questions, and collecting the answering condition of the user; performing personalized content calculation according to the answer condition of the user to obtain a recommendation result; and presenting the program corresponding to the recommendation result to the user. According to the program recommendation method provided by the invention, a recommendation service mode which can be directly touched by an interface is provided for a user in cooperation with an answering mode of a client, and real-time recommendation of personalized contents is carried out according to real-time feedback of the user to questions, so that the role recognition precision of a recommendation system and the timeliness of recommendation feedback can be improved; and through accurate identification of the user portrait, favorite programs are pushed to the user in real time.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for recommending personalized programs based on an answering mode. Background technique [0002] Intelligent recommendation systems are used in all walks of life in the current era of information explosion. In the field of large screens, intelligent recommendation systems are widely used in recommending film and television program content for TV end users. Generally, recommendation systems play a role in this process. The role of support. Large TV screens are generally used by multiple users in the family, and have relatively high requirements for user identification and response timeliness of the recommendation system. Most of the existing recommendation systems do not have obvious user information collection entrances and high-timeliness recommendations. Content feedback, the existing recommendation system is not perfect in terms of role recognition and t...

AI Technical Summary

Problems solved by technology

Large TV screens are generally used by multiple users in the family, and have relatively high requirements for user identification and response timeliness of the recommendation system. Most of the existing recommendation systems do not have obvious user information collection entrances and high-timeliness recommendations. Content feedback, the existing recommendation system is not perfect in terms of role recognition and timely feedback

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