Abstract
Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.
Highlights
With the rapid development of high and new technology, science and technology have been more and more integrated into and affect our life
In order to improve user recommendation satisfaction, improve advertising conversion rate prediction accuracy, and accurately analyze online marketing effects, this paper proposes an online marketing effect analysis method based on multimodel fusion and artificial intelligence algorithm in the context of big data and verifies the effectiveness of the method in this paper through experiments
In order to achieve the mutual restriction between user multitask factors, on this basis, this paper adds the above fusion framework to multitask factors, which is conducive to sharing the shallow representation of users and forming a multitask fusion framework for user attribute inference. e specific user attribute inference method is shown in Figure 2. e two stages of this framework complete the following tasks, respectively: in the first stage, single model inference, userlevel vector representation is realized by using the learning methods based on text semantics and keyword based on text word frequency (NW_TF-IDF) proposed in Section 3 according to user data, and M feature distribution probabilities of users are trained through the model
Summary
With the rapid development of high and new technology, science and technology have been more and more integrated into and affect our life. In recent years, the research and application of artificial intelligence in customer service, marketing, and other fields have gradually been deepened, bringing opportunities and challenges to this industry and market. In this era of rapid technological development, the pace of society’s functioning, our ability to process information, and the impact of technology on social progress are all accelerating at an unprecedented rate. Smart marketing uses human creativity to create advanced computers, networks, mobile Internet, Internet of ings, Security and Communication Networks integrated technology, etc., and apply them to new thinking, new ideas, new methods, and new tools in the field of contemporary brand marketing. In order to improve user recommendation satisfaction, improve advertising conversion rate prediction accuracy, and accurately analyze online marketing effects, this paper proposes an online marketing effect analysis method based on multimodel fusion and artificial intelligence algorithm in the context of big data and verifies the effectiveness of the method in this paper through experiments
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