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.

Consumer purchasing behavior varies according to the type of product being considered. Notably, these behavioral differences become more salient depending on whether a product fulfills a utilitarian function or offers hedonic gratification. The present study aims to develop a novel product classification model that categorizes contemporary goods and services based on the extent to which they possess hedonic or utilitarian value. Methodologically, the study employed a two-stage approach. In the first stage, to identify appropriate products for inclusion in the classification model, the researchers conducted focus group interviews, a comprehensive literature review, and an analysis of major retailers’ websites. In the second stage, a survey was administered to a sample of 1,104 adult consumers to refine and validate the product classification model derived from the selected items. Consequently, the study produced two hedonic–utilitarian product grids: one comprising eighty-eight specific product examples, and the other encompassing thirty-five broader product categories.