There are many startups attempting to build content recommendation and curation algorithms. Their approches are varied and range from the simple to the very complex.
I would generally categorize the simple approach as using explicit profiles. Explicit profiles are built by asking directed questions (What are you interests?) and tracking usage (What articles have you read?)
The complex approach is to build implicit profiles. Implicit profiles are based on what you do, but also what you don’t do. This requires a lot more understanding of the content characteristics and mapping them back to the user profile. For example, you have be shown 10 different articles about a specific topic, but you only click on 1 of them. What was the reason you clicked on that specific article? What it the image? author? title? time of day? day of week? device (mobile/tablet/desktop)? What if you already read it from a different source?
Recommendation algorithms are designed to be safe. If you’re on Amazon looking at a book, you won’t be recommend a scooter. It will most likely recommend another book within the same genre and topic. This bodes well for a the explicit profile approach to work without the need a very strong implicit profile.
There are many other issues to overcome with content recommendations and curation, I don’t think implicit profiles will be the main hurdle.
I used the app this weekend and while very basic, it is very useful and the open API will be a valuable resource to anyone looking to build a transit/travel app.
In the beginning, there were a handful of sites where you would go to to seek information (CNN, ESPN, WeatherChannel etc.) As the amount of information grew, companies were developed to help categorize and sort information (Yahoo), then came search engines.
Today, we’ve had a massive shift in the way we consume content. We no longer have to pull or seek out content. The majority of content we consume now is pushed to us via email, Facebook, or Twitter.
As the number of friends and follows grow on the social platforms, the amount of information gushing through multiplies and we’ll need another tool to help sift through all that content.
Like many other startups, we use Amazon Web Services (AWS) to run Fitocracy’s website and iPhone app. Our main stack consists of half a dozen Elastic Cloud Compute (EC2) web instances, an EC2 Redis instance, and an Relational Database Service (RDS) instance that are all located in us-east-1…
One of the most defensible positions for a startup is if you can achieve the network effect. The network effect is so strong that it has kept large companies in business for a long time, despite bad products and numerous competitors. Craigslist is a perfect example. It is only recently that a…
The billion dollar tech startup valuations and acquisitions are starting to attract a lot of opportunist to the industry. A lot of these people are not technical and they know they need a CTO to get to get to MVP.
I don’t have any problem with non-technical founders teaming up with…
I tried out Twitter’s newly rolled out Discovery tab to see if it could do as good a job as Knowaboutit or News.me (two social media automated curation / content discovery newsletters that I had been recently using). This is what “filtered” to the top: