Sunday, January 27, 2013

We shall fellow Prismatic

Prismatic is a news recommendation site created by several Berkeley students. Three of them are the famous Phd students in Berkeley: Aria Haghighi (work with Dan Klein on NPL, and also worked with Andrew Ng as undergraduate student in Stanford), Jenny Finkel (work with Chris on NPL) , and Jason Wolfe (work with Stuart Russell on AI Lab). Recently, it received an A round 15 million dollor from Jim Breyer and Yuri Milner to "Attack The Impossible Problem of Bringing You Relevant News". 

 In fact, before Prismatic, there were lots of tries to solve the complex problem: "What i shall read now". Including some not so successful works like Zita, Pulse, Flipboard, Digg, StumbleUpon, and Wavii. The core challenge of this problem is that it is still impossible for computers to understand the real content of a news or understand the needs of human being, besides, the timing is also tricky: what the readers want to read now is clearly less rhythmic than what the readers want to read eventually. 

Lots of people think that Prismatic is only another immature try on this impossible problem. However, in my mind, Prismatic has kind of grasp the core of this type of applications.

  1. Using social network. Ask computers to understand real human is not possible, but your friends are possible. Grasp enough materials from your social network is critical for solving this problem.
  2. Analyzing persons instead of contents. Persons have different kinds of tags: a geek, an artist, a student, or even a news report etc. Among all the geeks, there are still lots of subtypes. News and articles have different weights for different group of people. Besides, the interaction between your system and a person can future affect the system in a stable way.
  3. Strong academic background. Machine Learning and NLP are still hot research topics today. Phd. students in the best university will be more easy to deploy the obvious 'better' system.

We shall fellow Prismatic? 

Is that too hard for our ordinary programmers who do not obtain any ML or NLP doctor degree or do not study in the world's best laboratory or university. The answer is Yes, but there are other ways: "There are a simple version of Prismatic". 

For example, I am a social network addictive person. Each day i spend at  least one hour (i call it lunch-social time) on different social network: Sina Weibo (a twitter like micro-blog system), Facebook, AcFun (an interesting video sites like youtube), and many others. Besides this hour, i do not like the social medias disrupt my work. So, in the first quater of the lunch-social hour, there are always interesting posts or news i like and read, but after around 15 minuts, i usually found there is not any new content, and i will spend next 45 minuts flushing the page again and again trying to get one interesting page (Of couse, all the posts i read during this lunch-social hour wound not be serious, they often are some jokes, break news, funny comic or popular short videos). How to help me? You need:


A Simple Version of Prismatic:

  1. Narrow down your contents only in funny stuff. "Funny comic, video, story", "Breaking news", "Social Trends" etc, other high quality blogs or posts can be left to Prismatic.  People usually do not criticize your system harshly  when given not so accurate recommendation while finding fun.
  2. Classify your users into pre-defined classes by human instead of unsupervised learning by computers. A heavy social network users can easily predict whether a post would be popular or not. Humans are really better than computers in such problems.

I would like to  try it any way not matter how hard it is because i want to use such tool for my launch-social time. :)


There are some reference materias about Prismatic. You can check it out if you are also interested in: 

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