Advertisement recommendation method and apparatus

Abstract

The present invention relates to an advertisement recommendation method, comprising: receiving an advertisement recommendation request; acquiring a user tag that is modified according to feedback information and that corresponds to a user in the advertisement recommendation request, wherein the feedback information comprises positive feedback information and negative feedback information; screening out a candidate recommendation advertisement according to the modified user tag; calculating, according to an estimated click model, an estimated click possibility of each candidate recommendation advertisement for the user on a current display page; calculating, according to an estimated non-interest model, an estimated non-interest possibility of each candidate recommendation advertisement for the user on the current display page; and screening out a target recommendation advertisement from the candidate recommendation advertisement according to the estimated click possibility and the estimated non-interest possibility. In an advertisement recommendation process, both positive feedback information and negative feedback information are considered, and advertisement recommendation accuracy is improved. Further, an advertisement recommendation apparatus is further provided.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to an advertisement recommendation method and device. Background technique [0002] With the development of Internet technology, obtaining information through the Internet, life, entertainment and work have become a part of people's lives. In order to increase their popularity and promote their products, merchants often place advertisements through the Internet. [0003] Existing advertising recommendation methods are often based on the user's basic information and the method of modeling the click behavior of the individual to realize the advertisement recommendation, but this recommendation method does not consider the deep mining of the user's true emotional orientation and interest in the advertisement, especially It is difficult to identify the user's negative emotions, and even the behavior of the user's negative emotions may be identified as the behavior that the us...

AI Technical Summary

Problems solved by technology

[0003] Existing advertising recommendation methods are often based on the user's basic information and the method of modeling the click behavior of the individual to realize the advertisement recommendation, but this recommendation method does not consider the deep mining of the user's true emotional orientation and interest in the advertisement, especially It is difficult to identify the user's negative emotions, and may even identify the behavior of the user's negative emotions as the behavior that the user is interested in. The recommended advertisements are difficult to avoid harassing the user, and the accuracy of the advertisement recommendation is not high

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