Reading Recommendations From People You Trust

Voracious readers, don’t you love seeing a list of Must Reads from someone you trust, who has interests similar to yours? Whenever I see such a list I get all excited, login to half.com and buy most of them. I’m a big amazon fan and find the ratings and reviews very useful, but there is no substitute for that recommendation from someone you admire.

Note that people you respect and admire is not necessarily the same as the list of your friends, or is even a logical community or group on facebook or linkedin. It often isn’t someone you know personally.

Recommendations often come from blog posts, but they are scatter-shot. One of my favorite bloggers is Brad Feld a VC at the Foundry Group. He posted a list of his favorite books about a month ago, and I gobbled them up. He (fortunately) has a Books link on his website.

So this is the OneStop Secret Sauce blog, mostly about Building Web Community (@Sun). How do reading recommendations fit in?

Isn’t this a natural for some sort of community? It’s not a community as per facebook or linkedin, but there should be some way to consolidate reading recommendations. Therefore, we’ve established a community on SunSpace called “READ”, and we’ve seeded it with the reading lists from a bunch of notables.

For this community we hope to leverage the organization, in particular roles and job titles. The assumption is that many people will care about the books a Principal Engineer is reading, at least the technical ones. I’m interested in the business books that the execs are reading. To take this a step farther, I also care about the news sources (blogs, etc.) that the people I respect consume.

It’s important to include meta information about the books, particularly date and a short description. Categorization (and soon tags) will help a lot.

The tables below are on the Mike Briggs page of the READ community, categorized into Daily News, Business Related, and Fiction or Just Good Reads,  Sorry, the community is only available on the Sun internal network. Maybe Brad will do one for his large external audience. :)

Daily News

Name Description
TechCrunch Ode to Mike Arrington. The emphasis is “obsessively profiling and reviewing new Internet products and companies”
Business Insider started as the Silicon Alley Insider. Run by Henry Blodget, the former equity research analyst, now barred from the securities industry. This one is my favorite and the one I read first.
TechMeme includes current popular stories (particularly blog postings) on technology. Story selection is accomplished via computer algorithm extended with direct human editorial input.
GigaOm launched by Om Malik in 2006. Known for providing in-depth analysis of developing news stories

Business Related

Title Author Published Description
Googled: The End of the World as We Know It Ken Auletta Oct 2009 The best google book I’ve read by far. Thoughtful and content full
A Colossal Failure of Common Sense: The Inside Story of the Collapse of Lehman Brothers Lawrence McDonald July 2009 Learn a lot about Wall Street from a real trader. Very well written.
The Accidental Billionaires: The Founding of Facebook. A Tale of Sex, Money, Genius, and Betrayal Ben Mizrich July 2009 Very simplistic. A fast read. Not Mezrich’s best effort.
Behind the Cloud: Untold Story of Salesforce.com. Marc Benioff Oct 2009 Awesome. Contains a catalog of how to leverage community and collaboration.

Fiction or Just Good Reads

Title Author Published Descriiption
South of Broad Pat Conroy August 2009 Every bit as good as Conroy favorites such as Prince of Tides, Lords of Discipline, and The Great Santini
The Guinea Pig Diaries A.J Jacobs Sep 2009 Jacob’s book Living Biblically is also a fun read.

Better Search with Predictive Typing: Can We Do it Better Than Google ?

Many of my favorite sites including google, linkedin, and netflix now have excellent predictive typing support. It strikes me that we should be able to do the same thing on SunSpace, and maybe do it better.In the case of Google, the suggestions returned will be what others are searching for. This list is  influenced  by your own search history.

The Netflix engine searches the universe of Movies and Actors. The list displayed is influenced by the popularity.

Your Linkedin type ahead universe is populated mostly by your connections.

We should be able to take the best attributes of the above!

On SunSpace we have more information to work with. Like Netflix we are searching a bounded universe. There are tens of thousands of movies and actors to select from, verses hundreds of millions of documents in the google index. In SunSpace we have less that 150,000 objects to work with, and lots of information on each object.

On SunSpace we know. (This is all tracked via opaque handles)

  1. Search terms used, for you and for all users on the system.
  2. The documents and wiki pages that were found via search and the search terms used, for you and for all users on the system.

Thanks to Community Equity we also know a lot about each user and each wiki page and document in the system.

  1. Information Value of each document or wiki page.
  2. Meta data for each document and wiki page. (author, tags, last modified, etc.)
  3. Equity value for each user. (based mostly on Information Value of the documents they own.)

As an example, let’s say we are searching for Cloud Computing. Google gives the the following list of recommendations:

Cloud Computing
Cloud Computing Companies
Cloud Computing Wikis
Cloud Computing Leaders
Cloud Computing Stocks
Cloud Computing Architecture
etc.

In SunSpace we can provide you not only with the recommendation of the search term, but also

  1. the most popular cloud computing documents people have found with search
  2. cloud computing documents with the highest information value
  3. interesting meta data for each document.

I believe that people generally utilize type ahead as a time saver, verses as a recommendation engine, so we need to make sure the “expected” results appear at the top of the list. If a user as typing in “cloud computing” in a prior search, that term, and the document the user selected from the results list should appear first.

As a bonus, we can match against the corporate directory in real time, and provide phone numbers and locations for individuals. This is normally a 10-20 second endeavor using the IT supported tool.

Posted at 06:09AM Dec 29, 2009 by Michael Briggs in Sun  |  Comments[0]