You're welcome to make a startup based on that idea ;) up here in Canada where we have real seasons I'll find you some great test users!
(Although I've been to the Bay Area and it's not without weather challenges)
It would be better to train a classifier to learn what clothes fit the current trend.
https://medium.com/@Pinterest_Engineering/taste-graph-part-1...
How is this different/better than what they are doing?
Just a heads up.
Not that it matters, as I'm certain other companies use more modern techniques (deep learning) for "outfit advice".
- We do use DL. I didn't mention it because it is not relevant in the context of the post;
- How we use DL. One of the jobs of the graph is to tell the DL algorithm what content needs what type of descriptors. The graph can do this thanks to the different levels in our ontology, and because it understands our content in its context: it understands people's interaction and tagging. DL is a small part in our entire infrastructure;
- The DL market. Lots of companies use DL to identify attributes in an image, and the level they achieve is impressive. Having had long discussions with the best of these companies, I can tell you that building the correct ontology is nearly impossible without the entire infrastructure. We are happy building the intelligence that tells DL what to do, and then attaches descriptors correctly to outfits and taste profiles;
- Our patents. They cover a few relevant aspects in the online fashion market: a system to tag fashion images with shoppable products; a system and method to capture/understand how people mix and match clothes in outfits and closets; and more.
