Closed Search Domain vs. Open Search Domain

When using traditional RSS aggregators, the user supplies the list of RSS feeds. This means that the domain of information gathered by a traditional RSS reader/aggregator is limited to the RSS feeds that are known to the user.  I call this a closed search domain. However, in an environment such the one we have today where thousands of new content sources are being created on a daily basis and anyone can potentially become a publisher, it is unrealistic to put the burden on the user to keep up with the thousands of new content sources that are sprouting up each day.  Filtrbox takes this burdensome responsibility away from the user and discovers the new content sources for the user because Filtrbox’s search domain covers all the new content sources. I call this an open search domain. The user can also add RSS feeds to the search domain, thereby guaranteeing that their RSS feeds of interest are searched. This approach leads to the user discovering new content sources.

Publisher centric vs. Content centric

Traditional RSS readers/aggregators present to the user all the content that is published by a specific publisher regardless of whether the user is interested in the content or not. Thus, the traditional RSS readers/aggregators implement a publisher centric information consumption model. On the other hand, Filtrbox implements a content centric information consumption model.  Rather than deliver to the user all the content published by a specific publisher, whether its relevant or not, Filtrbox allows the user to filter for the content that they are interested in from ANY publisher by providing contextual keywords. The content centric model implemented by Filtrbox greatly reduces information overload because each piece of content is examined and filtered for contextual relevance before it is delivered to the user.

No filtering vs. Contextual relevance filtering

As indicated above, traditional RSS aggregators do not filter the content.  All content published by a publisher in the user’s closed search domain is delivered to the user regardless of whether it is relevant or not.  Filtrbox applies algorithms that filter content from an open search domain of publishers for contextual relevance.  Filtrbox uses multiple factors to determine the contextual relevance of content and assigns a score called FiltrRank.  The most important feature of the algorithm is that the contextual relevance algorithm learns from a Filtrbox user’s implicit interests and applies the implicit interest to future contextual relevance filtering. This means that the content delivered to the user is content that that specific user is interested in and not content other people are interested in.  Contextual relevance filtering plays a large part in the reduction of information overload.

Beyond RSS

Unlike traditional RSS readers/aggregators, Filtrbox consumes content delivery formats beyond RSS. Filtrbox is capable of consuming both standard and proprietary content delivery formats.

For example, if you only want to follow Lopez Lomong, you can set up your widget to show news about Lopez only or if you care about Women Gymnastics only, you can set up your widget to show you news about Women Gymnastics only.   In addition, you can set up different combinations of athletes and sports.  Most olympic content consist of every bit of news about the Olympics and you have to do the filtering for the news that you are interested in. At Filtrbox, we do the filtering for you.

We have created two types of widgets, a blog widget to embed on your blog and a desktop widget that runs on your desktop.  Both widgets can be found here.

My favorite widget is the desktop widget and because its my favorite, it has an additional special page for itself here.

Download the widgets and enjoy the Olympics.

Join a dynamic, growing software company in Boulder, Colorado.
Basic requirements are:

* Solid experience with Java and LAMP
* System administration skills (a plus)
* Working knowledge of Information Retrieval and/or NLP (a plus)
* Must be energetic, motivated and creative

Please send your resume to TOM AT FILTRBOX DOT COM

NOTE: Prima Donna, high maintenance Rockstar developers, please do NOT bother sending your resumes!!!!

On Thursday (04/24/2008 ) last week, I had the privilege of talking to Dr. Jim Martin’s Natural Language Processing (NLP) graduate class, at the University of Colorado at Boulder, about the work that we are  doing at Filtrbox and the role that current NLP students will play in the future of information technology.  This blog post is the basis of my message to the class.

As I have written before, the problem that we face today is how to harness the data that is available on the web so that we can apply meaningful interpretation to it using applications.  This problem is rooted in the assumption that the data that is stored on the web is “unstructured”.  Unlike the majority of the data processed by applications today which is stored in some form of a structure e.g. a relational database, the data on the web is not so, as its is perceived as discrete pieces of data scattered all over the web.

I told the class that part of what I am doing at Filtrbox is an attempt to prove that the data on the web is not as “unstructured” as we may think today.  Within that data, there is a lot of structure, relationship and general interconnectedness no matter how “discrete” we may think it is.  With effective mining of the data and good applications, we can apply interpretation to the data and produce meaningful information.  However, we are still far from applications that can apply effective interpretive meaning on this data.  The reason for this is that we have to address the problem of information retrieval (IR) first before we can get to the writing of applications. 

To recognize where we are today on the continuum of web data information retreival and applications; a look at the evolution of enterprise applications gives us a great analogy:

Enterprise applications are where they are today primarily because they have a structured data storage model (Relational Database or RDB) and a standard access model (Structured Query Language or SQL).  Before there were enterprise applications that we know today, there were only RDBs and SQL.  While RDB work dates back to the 1960s, the RDBs that the majority is familiar with today had their beginnings in the 1970s.  The first (or widely believed to be) commercially available implementation of RDB+SQL was Oracle, then known as Relational Software, in 1979. This provided the ability to query an RDB for data using SQL but no applications as we know them today.  Analogizing this with the web, this is where we are today. We can go on Google or our favorite RSS readers (RDB analogy) and query for web data using a weak REST API or search form (SQL analogy) but we have no applications comparative to what is in enterprise today to interpret that data.  So simply put, today we are where enterprise applications were in 1979.

My message to the class was that applications like Filtrbox are starting to barely scratch the surface with respect to the implementing of applications on top of web data.  That is because, although its 2008, we are still in 1979.  The stumbling block is the perception of the “unstructured” nature of web data. Today’s NLP students will play a large role tomorrow in identifying and establishing structure in the “unstructured” web data in order to move us beyond 1979.

*Working knowledge of Flex 
*Solid experience creating Web 2.0 UIs 
*Actionscript 2 or 3 (must be recent) 
*Flash 8 or 9 (must be recent) 
*RoR/PHP/CSS/javascript/java skills a plus 
*Must be energetic, motivated and creative 

The following questions were addressed during one of the early TechStars panels:

1) What kills most startups?

• Surprise!! Surprise!! Not making money is not usually the big issue that causes failures unless you don’t have a vision
• Team dynamic issues – startup failures are mostly caused by founding team friction
• Companies fail due to execution failures. Execution failures are still team issues that can be categorized as follows: 

1.  Team dysfunction issues
2.  Team poor performance issues

1 and 2. are a “chicken and egg” situation

• Do not be afraid to address team issues head on, solve them and remove the problem
• Once you have a team issue problem that threatens your startup, re-adjust what you are doing or join another team (None of the original TechStars team members changed teams)
• Beware of meandering, where after several weeks you are not getting anywhere. Address and re-adjust immediately because you risk team members losing passion because you are not getting anywhere

 2)  “Getting acquired” as a business model

• Getting acquired is not a business model.  It’s a WISH!!!
• Concentrate on building a business that has compelling value

 3) The “style” of a startup

• You have the permission to create your own identity
• Have an attitude
• Have a style
• Develop a style for your startup and yourself and work it (America’s Next Top Startup, anyone???) all the way through

 MORE TO COME …..

The following questions were addressed during one of the early TechStars panels:

 1)     Do you need a brilliant idea before starting?

• NO!!! 
• You just need to get going.  If you ask too many people before you start and you get feedback, you are probably selling yourself short. Just start.
• Look for analogies in paradigms.  The first internet revolution was trying to implement an analogy of the non-digital world.  Seek the next analogy.   Also consider addressing areas that failed in the first internet revolution. 

2)     How do you know if you have an idea and you should step it up?

• When you start having people expressing need and people catching on.
• Listen – Listen to your peer group. Listen to the right people and the people that form your market. VCs are not necessarily the market.  
• Sometimes you have to provide what people are going to need tomorrow (the example that was given here was what Greg Reinacker did with the concept behind Newsgator).  When you ask people, they probably will tell you what they needed yesterday and not know what they need tomorrow.  So you have to be ahead of the ball game.
• There are two ways to describe how startup ideas evolve                                                                

1. Scratch someone else’s itch                                                         
2. Scratch your own itch

Ultimately you have to move from 2. to 1.  

(My interpretation of what Brad Feld was saying here is that you either have to solve problems that you are having or problems that other people are having.  But to be a successful you have to end up solving problems that other people are having if you want your idea to get off the ground)

3)     Should you start a startup in the consumer space or the B2B space?

• Relatively indifferent to consumer or B2B. The question is how you are extracting money , long term, from the people using your product
• At this stage, getting a great service up and running is important but most importantly you need to think of how you will make money in the long term
• Extracting money from the customer is an engineering problem. You are thinking about the “architecture of your business”.  Address how you interact with the customer for money. The internet is free but you have to work on something monetizable
• Some people who think that they are addressing the consumer internet now, may end up with a business solution.  Keep your mind open.

MORE TO COME …..