Personalized Taste and Cuisine Preference Modeling via Images
This work addresses the need for personalized recommendations in food and cuisine domains, but it appears incremental as it builds on existing computer vision and recommendation techniques without introducing major innovations.
The paper tackles the problem of modeling personal taste and cuisine preferences by analyzing food images from social media, using computer vision to extract insights and build individual profiles, with the goal of enabling more precise personalized recommendation systems.
With the exponential growth in the usage of social media to share live updates about life, taking pictures has become an unavoidable phenomenon. Individuals unknowingly create a unique knowledge base with these images. The food images, in particular, are of interest as they contain a plethora of information. From the image metadata and using computer vision tools, we can extract distinct insights for each user to build a personal profile. Using the underlying connection between cuisines and their inherent tastes, we attempt to develop such a profile for an individual based solely on the images of his food. Our study provides insights about an individual's inclination towards particular cuisines. Interpreting these insights can lead to the development of a more precise recommendation system. Such a system would avoid the generic approach in favor of a personalized recommendation system.