No abstract

Advertising has been one of the most effective and valuable marketing tools for many years. Utilizing social media networks to market and sell products is becoming increasingly prevalent. The greatest challenges in this industry are the high cost of providing content and posting it on social networks, maximizing ad efficiency, and limiting spam advertisements. User engagement rate is one of the most frequently employed metrics for measuring the effectiveness of social media advertisements. Previous research has not comprehensively analyzed the factors influencing engagement rate. To this end, it is necessary to investigate the impact of various factors (such as user characteristics, posts, emotions, relationships, images, and backgrounds, among others) on engagement rate because assessing these influential factors in different networks can increase the engagement of users with advertising posts and thereby increase the success rate of targeted advertising. To predict the user engagement rate, we extract the significant attributes of posts and introduce an adaptive hybrid convolutional model based on FW-CNN-LSTM. We cluster the selected data based on the weight and significance of their attributes using the FCM and XGBoost algorithms and then apply CNN- and LSTM-based methods to select similar features. Using accuracy, recall, F-measure, and precision metrics, we compared our algorithm to standard techniques such as SVM, Logistic regression, Naïve Bayes, and CNN. According to the findings, hashtag, brand ID, movie title, and actors achieve the highest scores, and the values for actual training time in various data ratios are relatively linear, which confirms the scalability of the proposed model for large datasets. The results also demonstrate that our proposed method outperforms others and can lead to targeted ads on social media.

No abstract

Sales forecasting is one of the significant issues in the industrial and service sector which can lead to facilitated management decisions and reduce the lost values in case of being dealt with properly. Also sales forecasting is one of the complicated problems in analyzing time series and data mining due to the number of intervening parameters. Various models were presented on this issue and each one found acceptable results. However, developing the methods in this study is still considered by researchers. In this regard, the present study provided a hybrid model with error feedback for sales forecasting. In this study, forecasting was conducted using a supervised learning method. Then, the remaining values (model error) were specified and the error values were forecasted using another learning method. Finally, two trained models were combined together and consecutively used for sales forecasting. In other words, first the forecasting was conducted and then the error rate was determined by the second model. The total forecasting and model error indicated the final forecasting. The computational results obtained from numerical experiments indicated the superiority of the proposed hybrid method performance over the common models in the available literature and reduced the indicators related to forecasting error.