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11th International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Zhang, Yongkang Fu, Sihan Taylor's University Selangor 200127 Malaysia DAWN Education Consulting Co. Ltd. Guangdong Foshan528200 China

With the rise of new media, people's understanding of advertising has also changed. Traditional advertising methods are difficult to attract people's attention, and the information conveyed by commercial advertising is becoming increasingly diverse. In the new media environment, users must rely on various media to have a more comprehensive understanding of advertising. The development of commercial advertising relies on various new media. New media is a new carrier and channel that creates conditions for the leapfrog development of advertising. Consumers can only have a more comprehensive understanding of an advertising brand through the joint integration of multiple media. Therefore, in the context of new media, based on consumer awareness and consumption habits of advertising, it is necessary to improve the efficiency of advertising design and dissemination, in order to better meet consumer needs. The commercial new media advertising recommendation technology based on CF (collaborative filtering) algorithm has great potential and advantages. Through personalized recommendations, the click through rate and conversion rate of advertisements can be improved, bringing better advertising effects to enterprises. In the future, with the continuous development of data mining and artificial intelligence technology, advertising recommendation technology based on collaborative filtering algorithms would be further improved and applied, bringing greater development space for commercial new media advertising. The experimental results showed that after implementing the recommendation algorithm, the attention level of the advertisement was above 0.7, and the click through rate was as high as 0.8 or above. Due to the high accuracy and personalization of recommendation algorithms, the advertisements seen by users are more in line with their interests and needs, thereby increasing their attention and click through rate towards advertisements. © 2024 The Authors. Published by Elsevi

关键词： advertising recommendation algorithm Business New Media Collaborative Filtering algorithm Data Analysis

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Commercial New Media advertising recommendation algorithm Based on Data Analysis

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Procedia Computer Science 2024年 247卷 537-544页

作者： Yongkang Zhang Sihan Fu Taylor's University Selangor 200127 Malaysia DAWN Education Consulting Co. Ltd. Foshan 528200 Guangdong China

关键词： Business New Media advertising recommendation algorithm Data Analysis Collaborative Filtering algorithm

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