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The classification and recommendation system for identifying social networking site (SNS) users’ interests plays a critical role in various industries, particularly advertising. Personalized advertisements help brands stand out from the clutter of online advertisements while enhancing relevance to consumers to generate favorable responses. Although most user interest classification studies have focused on textual data, the combined analysis of images and texts on user-generated posts can more precisely predict a consumer’s interests. Therefore, this research classifies SNS users’ interests by utilizing both texts and images. Consumers’ interests were defined using the Curlie directory, and various convolutional neural network (CNN)-based models and recurrent neural network (RNN)-based models were tested for our user interest classification system. In our hybrid neural network (NN) model, CNN-based classification models were used to classify images from users’ SNS postings while RNN-based classification models were used to classify textual data. The results of our extensive experiments show that the classification of users’ interests performed best when using texts and images together, at 96.55%, versus texts only, 41.38%, or images only, 93.1%. Our proposed system provides insights into personalized SNS advertising research and informs marketers on making (1) interest-based recommendations, (2) ranked-order recommendations, and (3) real-time recommendations.

Abstract. The current study adopts a broad-based model of media and audience values as well as recent understandings of enjoyment versus appreciation to predict that aggregate audience appraisals should be related to film budget, gross, and measures of viewership differently depending on the type of appraisal elicited. Data suggest both enjoyment and appreciation are positively related to measures of aggregate selective exposure when controlling for film budget. This finding challenges a view that appreciation is negatively related to success. Discussion centers on implications for understanding potential functional aspects of audience appraisals and suggests future research on audience morality and media production.

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