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.

The most typical problem in an analysis of natural language is finding synonyms of out-of-vocabulary (OOV) words. When someone tries to understand a sentence containing an OOV word, the person determines the most appropriate meaning of a replacement word using the meanings of co-occurrence words under the same context based on the conceptual system learned. In this study, a word-to-vector and conceptual relationship (Word2VnCR) algorithm is proposed that replaces an OOV word leading to an erroneous morphemic analysis with an appropriate synonym. TheWord2VnCR algorithm is an improvement over the conventional Word2Vec algorithm, which has a problem in suggesting a replacement word by not determining the similarity of the word. After word-embedding learning is conducted using the learning dataset, the replacement word candidates of the OOV word are extracted. The semantic similarities of the extracted replacement word candidates are measured with the surrounding neighboring words of the OOV word, and a replacement word having the highest similarity value is selected as a replacement. To evaluate the performance of the proposed Word2VnCR algorithm, a comparative experiment was conducted using the Word2VnCR and Word2Vec algorithms. As the experimental results indicate, the proposed algorithm shows a higher accuracy than the Word2Vec algorithm.

The ability to automate and personalize the recommendation of multimedia contents to consumers has been gaining significant attention recently. The burgeoning demand for digitization and automation of formerly analog communication processes has caught the attention of researchers and professionals alike. In light of the recent interest and anticipated transition to fully autonomous vehicles, this study proposes a text–image embedding method recommender system for the optimization of personalized multimedia content for in-vehicle infotainment. This study leverages existing pre-trained text embedding models and pre-trained image feature extraction methods. Previous research to date has focused mainly on textual-only or image-only analyses. By employing similarity measurements, this study demonstrates how recommendation of the most relevant multimedia content to consumers is enhanced through text–image embedding.