Document Type

Article

Publication Date

3-1-2025

Journal / Book Title

Analytics

Abstract

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.

DOI

10.3390/analytics4010004

Rights

© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Published Citation

Choi, J.-A., Hong, T., & Lim, K. (2025). Personalizing Multimedia Content Recommendations for Intelligent Vehicles Through Text–Image Embedding Approaches. Analytics, 4(1), 4. https://doi.org/10.3390/analytics4010004

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