Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

Lee, SoHyun; Yoon, Sang-Hyeak; Kim, Hee-Woong

doi:10.1109/access.2021.3056115

Cited by 11 publications

(3 citation statements)

References 39 publications

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“…In the literature, various approaches were used for the prediction such as, neural network-based intelligent decision-making [24], deep learning models [25][26][27][28], machine learning-based methods [29][30][31][32], and CNN, RNN and LSTM based methods [33][34][35].…”

Section: Rnn Modelmentioning

confidence: 99%

A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

et al. 2023

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Financial data are a type of historical time series data that provide a large amount of information that is frequently employed in data analysis tasks. The question of how to forecast stock prices continues to be a topic of interest for both investors and financial professionals. Stock price forecasting is quite challenging because of the significant noise, non-linearity, and volatility of time series data on stock prices. The previous studies focus on a single stock parameter such as close price. A hybrid deep-learning, forecasting model is proposed. The model takes the input stock data and forecasts two stock parameters close price and high price for the next day. The experiments are conducted on the Shanghai Composite Index (000001), and the comparisons have been performed by existing methods. These existing methods are CNN, RNN, LSTM, CNN-RNN, and CNN-LSTM. The generated result shows that CNN performs worst, LSTM outperforms CNN-LSTM, CNN-RNN outperforms CNN-LSTM, CNN-RNN outperforms LSTM, and the suggested single Layer RNN model beats all other models. The proposed single Layer RNN model improves by 2.2%, 0.4%, 0.3%, 0.2%, and 0.1%. The experimental results validate the effectiveness of the proposed model, which will assist investors in increasing their profits by making good decisions.

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Section: Rnn Modelmentioning

confidence: 99%

A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

et al. 2023

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“…The model has stronger expressive ability. In the deep learning recommendation system, the accuracy of advertising click rate prediction can be improved by changing the complexity of neural network (Lee et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The analysis of green advertisement communication strategy based on deep factorization machine deep learning model under supply chain management

Zhu

Jia

et al. 2023

Expert Systems

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Artificial intelligence (AI) technology has brought new reconstruction opportunities for the intelligence of the advertisement industry through the help of AI technologies such as machine learning and deep learning. First, the relationship between AI and the attractiveness of green advertisements is investigated, and the influence of different AI technologies in green advertisements on consumers' perception of the attractiveness of green advertisements is summarized. Second, based on the green advertisement dissemination rate data set, the data visualization exploration is carried out, and the data deletion and coding processing are carried out aiming at different characteristic variables. Finally, according to the problems existing in the current green advertisement communication and the high‐dimensional and sparse characteristics of the communication rate data set. In this paper, based on Deep FM (Factorization Machine), Gradient Boost Decision Tree (GBDT) is added to assist the experiment, and the prediction performance of green advertising communication is tested. The results are as follows. (1) Different AI expressions in green advertisements will affect consumers' perception of the attractiveness of green advertisements. (2) The prediction ability of Deep FM model after feature engineering is better than that of data cleaning only. The prediction effect of the model is obviously improved. The purpose of this paper is to integrate green advertising media communication into the ecological concept of harmonious coexistence between man and nature, strengthen the political belief of ecological civilization construction, and conform to the communication trend of today's severe ecological situation.

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“…Online video advertisements (OVAs) represent one of the fastest-growing platforms of social media advertising. Online video advertising, also termed Internet advertising and digital advertising by Lee et al (2021), is advertising that uses Internet technologies to publish videos containing information on products and services. One main feature of OVAs that attracts the interest of marketers and advertisers is the ability of viewers to share OVAs across multiple online platforms.…”

Section: Introductionmentioning

confidence: 99%

Gender and Age Differences in the Sharing Behavior of Online Video Advertisements: The Case of Selangor, Malaysia

Zainordin¹,

Basha²,

Ann³

et al. 2021

ASS

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Gender and age are considered important demographic factors that influence consumers’ behavioral intention, especially pertaining to video advertisements. However, limited research has examined the impact of gender and age on consumers’ sharing behavior of online video advertisements (OVAs) in Selangor, Malaysia. The aim of this paper is therefore to examine the role of gender and age in the intention to share OVAs in Selangor. Survey data was collected from 180 Millennial respondents in Selangor and analyzed using t-test and ANOVA to detect gender and age differences in the intention to share OVAs. The results indicated that gender and age have significantly distinct impacts on the intention to share OVAs among Millennials in Selangor. Male Millennials were found have a stronger intention to share OVAs compared to females, whereas Millennials in the 19 to 24 age bracket showed a stronger intention to share OVAs than those in the 25 to 30 and 31 to 38 age brackets. The study's practical implications, limitations, and future research directions are discussed.

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Prediction of Online Video Advertising Inventory Based on TV Programs: A Deep Learning Approach

Cited by 11 publications

References 39 publications

A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model

The analysis of green advertisement communication strategy based on deep factorization machine deep learning model under supply chain management

Gender and Age Differences in the Sharing Behavior of Online Video Advertisements: The Case of Selangor, Malaysia

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