Why recommendations are hard
I previously wrote a note about recommendations and addressing the fact that they aren’t always accurate. I wanted to follow up and briefly explain why.
The main problem comes from weather conditions that are incongruous with common expectations of accompanying temperatures. For example, it’s very easy to recommend shorts when it’s hot (temperature) and sunny (condition) out. We tend to think of this as fairly common summer weather. It’s more difficult, however, to recommend something when it’s hot and also hailing, as it was last week in New York. A raincoat would be a reasonable recommendation from a computer, but a human would know that’s going to be a bit uncomfortable. We can’t automate “wait inside until the summer squall passes.”
Addendum: A related difficulty with recommendations is that during many days there can be a sizable swing between the high and low temperatures, (e.g. hot days and cool nights). You can offer some recommendations for the high and some for the low, or you can take the average of the two and base your recommendations on that. Alternatively you could get the current temp and base the recommendations on that, which would be somewhere between the high and low. None of these methods is perfect, and some are more difficult to implement than others.
A secondary problem is simply that clothes don’t come with guidelines for when it’s appropriate to wear them. The products are picked from Svpply and put into buckets based on temperature and condition. Sometimes my picks are off, and sometimes these buckets are just too broad to be perfectly relevant to the conditions outside your window.
The solution is to find a balance between weighing temperature and condition, and we’re obviously not there yet. But we’re working on it.
