Abstract
Systems for connecting users with products in audiovisual content, such as videos or images, can have limitations in assisting a user to replicate a holistic outcome, for example an entire room design, which may result in fragmented or redundant product suggestions. A described system can perform multimodal analysis of content to generate a structured model of a desired outcome. This outcome model may then be compared against a model of the user's current state, which can be derived from user-authorized data sources (e.g., images of a user's environment, purchase history), to identify an outcome gap of missing products. A commerce engine can then source personalized recommendations to address this gap, considering inferred user constraints such as budget or style preferences. The process can provide an approach to connect content-based inspiration with commerce recommendations that may be tailored to a user’s specific context and needs.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Coelho, Serline S., "System for Personalized Commerce Using Multimodal Outcome Gap Analysis", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12394