Advertisement recommending method and advertisement recommending server

Abstract

An embodiment of the invention provides an advertisement recommending method and an advertisement recommending server. The method comprises the steps as follows: obtaining webpage visit information and advertisement click information, wherein the webpage visit information is used for indicating n webpages visited by m users, and the advertisement click information is used for indicating x advertisements clicked by the m users on the n webpages; predicting click probabilities of the x advertisements when the i user in the m users visits the j webpage according to the webpage visit information and the advertisement click information; determining novelty factors corresponding to the x advertisements respectively; determining p advertisements to be recommended to the i user in the x advertisement according to the click probabilities of the x advertisements and the novelty factors corresponding to the x advertisements respectively. By means of the advertisement recommending method and the advertisement recommending server, the click rate of the advertisements can be increased, and the user experience can be improved.

Description

technical field [0001] The present invention relates to the field of information processing, and in particular, to a method for recommending advertisements and an advertisement recommending server. Background technique [0002] Internet online advertising has become the main way of advertising besides TV and newspapers. The income of online advertising is closely related to the click-through rate of the advertisement, and increasing the click-through rate of the advertisement is one of the effective ways to increase the income of the advertisement. In order to improve the click-through rate of advertisements, before recommending advertisements, it is necessary to predict the probability of users clicking on advertisements (hereinafter referred to as the click-through probability of advertisements). [0003] At present, two algorithms are mainly used to predict the click probability of advertisements to recommend advertisements to users. One is a recommendation algorithm ba...

AI Technical Summary

Problems solved by technology

It can be seen that compared with the algorithm based on CBF, although the CF algorithm improves the accuracy of the click probability prediction of the advertisement to a certain extent, and can improve the click-through rate of the advertisement, but because users often visit webpages with similar content, the CF algorithm is recommended. The advertisements given to the user are often very similar to the advertisements that the user is familiar with, and it is impossible to find the advertisements that the user is not familiar with but are potentially interested in, resulting in a low click-through rate of the advertisements and poor user experience

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