Advertisement recommendation based on personal interests and ad push fairness

Duen-Ren Liu*, Yu Shan Liao, Ya Han Chung, Kuan Yu Chen

*此作品的通信作者

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Purpose: Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required. Design/methodology/approach: The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website. Findings: The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach. Originality/value: Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

原文English
期刊Kybernetes
DOIs
出版狀態Accepted/In press - 1 一月 2018

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