Posts Tagged ‘recommendation system’

ReStream.me Mobile: The Twitter Discovery Engine Goes Where You Go

Tuesday, February 9th, 2010

Today is launch day for ReStream.me Mobile. Now you can access the ReStream.me discovery engine from everywhere. Highlight from the new web application include:

  • Filter your Twitter stream based on links and trends
  • Receive ReStream.me’s content recommendations
  • View the most popular content currently being shared on Twitter
  • Highlighted content keeps you updated on what your favorite Twitter users are publishing

ReStream.me Mobile is free. You just need a Twitter account to get started.

Mining Twitter for Gold

Tuesday, January 12th, 2010

Finding the 27% of Tweets that Have Value

A recent study by ReadWriteWeb has shown that only 27% of tweets contain information with some value. Many people will point to this and use it to dismiss Twitter as worthwhile platform. However, this number comes from Twitter’s flexibility. Some people use it to keep in touch with friends, others use it break news. Some use Twitter for advertising and others use it for sharing information they find on blogs.

It’s this last group that’s the most interesting. It’s the human web. It’s people finding information and sharing it that adds value where search engines can not.

The problem is finding the tweets that make up this 27% of the stream that holds information of value. Further, 27% doesn’t sound like much until you realize it’s 70+ million tweets per week. The best information on Twitter amounts to a needle in haystack.

This points to the growing need for filters and recommendation engines for the real-time web. Last week I posted on micro filters and I believe this post by ReadWriteWeb further emphasizes this need.

To leverage the value that Twitter and the whole real-time web hold, we need better tools. We need more filters that go beyond the basics; Twitter lists, follower lists, and individual favorites. For example, value can be attributed to the number of people sharing the same content or  the credibility and clout of those sharing it.

If the web is going to evolve beyond search, micro filters will play a huge part in it but filters alone are not the answer.

Recommendation systems are the other piece of the puzzle. They’re needed to understand user behaviors; what people like and don’t like, what they favorite, what they read, and what they share. Recommendation systems leverage this data and combine it with filters to find the best information that people want to read. This helps us to take a full advantage of the real-time web without becoming overwhelmed.

To solve the problem of finding the 27% of Tweets that have some value, filters will be used to narrow the stream of information. Then recommendation systems, which have some insight into our past behavior, will be able to narrow the focus even further by taking the information output by these filters and funnel it to us based on our interests. This means that we’ll all be giving up some privacy on the web but it’s a trade off we’ll need to make to keep up with the barrage of information.