The STORE framework can be extended to accommodate multimodal item content by integrating various types of data inputs, such as images, audio, and video, alongside textual features. This can be achieved through the following strategies:
Multimodal Embedding Generation: The framework can utilize specialized models for different modalities (e.g., CNNs for images, RNNs for audio) to generate embeddings that capture the unique characteristics of each data type. These embeddings can then be combined into a unified representation, allowing the STORE framework to leverage the rich information contained in multimodal data.
Joint Training: By employing a joint training approach, the STORE framework can learn to correlate different modalities. For instance, a model could be trained to predict the next item based on both user interactions and the associated image or audio content, enhancing the recommendation quality by considering the context provided by multiple data types.
Attention Mechanisms: Implementing attention mechanisms that can focus on relevant features from different modalities can improve the model's ability to discern important information. This would allow the framework to weigh the contributions of textual, visual, and auditory data dynamically, depending on the context of the recommendation task.
Task-Specific Tokenization: The semantic tokenization process can be adapted to create tokens that represent multimodal features. For example, tokens could be generated not only from text but also from visual features, enabling the model to understand and generate recommendations based on a comprehensive view of the item content.
User Interaction Modeling: The framework can incorporate user interactions with different modalities, such as clicks on images or plays of audio clips, to refine the recommendation process. This would allow the model to adapt to user preferences that may vary across different types of content.
By implementing these strategies, the STORE framework can effectively handle multimodal item content, enhancing its applicability and performance in diverse recommendation scenarios.