  Today, the acquisition of various behavioral log data has enabled deeper
understanding of customer preferences and future behaviors in the marketing
field. In particular, multimodal deep learning has achieved highly accurate
predictions by combining multiple types of data. Many of these studies utilize
with feature fusion to construct multimodal models, which combines extracted
representations from each modality. However, since feature fusion treats
information from each modality equally, it is difficult to perform flexible
analysis such as the attention mechanism that has been used extensively in
recent years. Therefore, this study proposes a context-aware multimodal deep
learning model that combines Bidirectional Encoder Representations from
Transformers (BERT) and cross-attention Transformer, which dynamically changes
the attention of deep-contextualized word representations based on background
information such as consumer demographic and lifestyle variables. We conduct a
comprehensive analysis and demonstrate the effectiveness of our model by
comparing it with six reference models in three categories using behavioral
logs stored on an online platform. In addition, we present an efficient
multimodal learning method by comparing the learning efficiency depending on
the optimizers and the prediction accuracy depending on the number of tokens in
the text data.
