we are using neo4j for our tinder for shopping app - phresh - here in la.
the hypothesis is that we can do better product recommendations than standard machine learning thats commonly used. further, the native graph db allows you to connect seemingly disparate data sets - eg social network, language, interest & taste graphs - that create connections only humans would be able to normally generate.
always looking for people interested in native graphs dbs.
yes, it is rare. hyperinflation occurs in destabilized economies that have too much printed cash relative to physical assets (to oversimplify a bit).
the US govt has printed too much money and keeps going further with stimulus packages and bailouts. the dollar is strong, currently, because it is the default fiat currency of the world. at some point the US will not be able to sell more bonds or the foreign creditors will dump dollars on the market or any number of other things can happen to trigger inflation.
you don't have to agree, but i would bet that hyperinflation will hit.
the 'current' analytics are for the current content. the 'previous' analytics are the sums for the spot (e.g. page 1, spot 1; page 1, spot 2; etc.) - per the faq.
you have statistics to base your submission decision on before spending any money.
In order to raise money you need to have a personal relationship with the respective funders. This is not a system you just waltz into and get capital all of a sudden.
Obviously, you try to get money from top tier firms and people. However, everyone's personal list of connections is different - resulting in different firms taking precedence.
If you are trying to raise money use your network. See who can refer you and at the same time back up your reputation.
I coincidentally met one of the principles of Dharma Merchant Services when I was considering which way to go. She was incredibly upfront and didn't push her service ('look at other options and let me know if we can help').
I try to make decisions relatively quickly when the consequences are mild for a sub-optimal solution so that attitude sold me.
I would take a look at the constraints you have in place and those that you can and/or want to change. Resources are the natural place to start the assessment.
Provide some more information as to your current (as well as past) work experience, education, interests, etc. and I am sure the community can be more concrete with suggestions.
the hypothesis is that we can do better product recommendations than standard machine learning thats commonly used. further, the native graph db allows you to connect seemingly disparate data sets - eg social network, language, interest & taste graphs - that create connections only humans would be able to normally generate.
always looking for people interested in native graphs dbs.
check out our job postings: https://angel.co/phresh/jobs