Multi-modal Generative Models in Recommendation System
<|reference_start|>Multi-modal Generative Models in Recommendation System: Many recommendation systems limit user inputs to text strings or behavior signals such as clicks and purchases, and system outputs to a list of products sorted by relevance. With the advent of generative AI, users have come to expect richer levels of interactions. In visual search, for example, a user may provide a picture of their desired product along with a natural language modification of the content of the picture (e.g., a dress like the one shown in the picture but in red color). Moreover, users may want to better understand the recommendations they receive by visualizing how the product fits their use case, e.g., with a representation of how a garment might look on them, or how a furniture item might look in their room. Such advanced levels of interaction require recommendation systems that are able to discover both shared and complementary information about the product across modalities, and visualize the product in a realistic and informative way. However, existing systems often treat multiple modalities independently: text search is usually done by comparing the user query to product titles and descriptions, while visual search is typically done by comparing an image provided by the customer to product images. We argue that future recommendation systems will benefit from a multi-modal understanding of the products that leverages the rich information retailers have about both customers and products to come up with the best recommendations. In this chapter we review recommendation systems that use multiple data modalities simultaneously.<|reference_end|>
@article{ramisa2024multi-modal,
    title={Multi-modal Generative Models in Recommendation System},
    author={Arnau Ramisa, Rene Vidal, Yashar Deldjoo, Zhankui He, Julian McAuley,
  Anton Korikov, Scott Sanner, Mahesh Sathiamoorthy, Atoosa Kasrizadeh, Silvia
  Milano, and Francesco Ricci},
    journal={arXiv preprint arXiv:2409.10993},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10993},
    primaryClass={cs.IR}
}