Eric Hayes, ehayes, -e

TechCrunch & Relevant Real-Time Recommendations

Not that I would normally post when someone covers us.  But today on techcrunch some interesting things were said.  Greg Linden of Findory.com did a great job of pointing out the difficulties in dealing with attention data in real-time. I would like to reiterate here what I commented back:

In late 2000, my team started working on an internet marketing technology designed to efficiently collect, organize, analyze, and pull metrics off of hundreds of thousands (to millions) of meta-data transactions per minute. That system was up, fast, scaled nicely, and proved its point by providing marketing lift and supplying outstanding real-time metrics without harassing the user! But… right technology, wrong time…

Today, that system (and its dozen man years of head start) is what we have based our attention streams on. The foundation of our system (client through server) is based on attention streams. We use it on the client to synchronize (and soon client side prioritization) articles among the multiple views (IE, Outlook, more…) we provide. We use it on the server side to synchronize client to server (to mobile). These efficient attention streams are what we will use (with permission and privacy protected) to aggregate users to make relevant recommendations (at the feed and article level), and add community based prioritization of your existing articles, and both timely enough to support today’s requirements.

Attention Streams vs. The Fire Hose

I just read Nick Finck’s Pruning the RSS Tree post at Digital Web Magazine. In the post Nick describes his painful experience slashing his RSS feeds down from 1,171 to 465. It struck me that what he really wants is one feed - the Nick Finck feed containing only up to the minute articles that are particularly interesting to him. At the same time I was struck by my perception that most of the interest surrounding Attention and the AttentionTrust has been focused on the marketing potential of using attention data to better target content and marketing messages to consumers.

But that’s only part of the story. What’s missing from the discussion are the specifics of how focusing on Attention can help individuals alleviate some of the enormous demands on our attention by cutting through information overload. And RSS isn’t helping with the problem. In fact it’s only increasing the flow. But the end goal of RSS technology shouldn’t be based on more is good. It needs to be tightly wrapped around the promise that less is more. We need to offer our RSS consumers a drinking fountain of information, not a Fire Hose!

Every day, millions of pieces of metadata are being generated by RSS news feeds from Websites and blogs. Every RSS feed and article contains metadata that can include information about its content. RSS metadata can include information about an RSS news feed article such as the source and date of creation. It can also include information about how users are interacting with the information by tracking which specific articles are being read and which articles are being ignored or deleted. An Attention Stream is created by combining information about the content with information about how the content is being consumed. By intelligently analyzing Attention Streams, including which articles are being read or ignored by the millions of people using RSS, new possibilities emerge to prioritize and recommend higher value content for users while cutting down on useless and duplicate information.

Think of an Attention Stream as a way of noticing all of the steps you take to gather and consume information.

From the obvious things like:

           Subscribing to a feed

Reading an article

Deleting a feed

Deleting an article

Deleting unread articles

Rating an article

Tagging an article

Clicking a link in an article

To the less obvious:

The time spent reading an article

Ignoring a feed or article

Deciding to have feeds available on multiple devices

Deciding to synchronize feeds and articles across devices

Clicking on update now to retrieve the latest articles from a feed

With an Attention Stream what you aren’t reading is just as important as what you are reading.

The first step is connecting the RSS user community with the right tools to gather and efficiently processes Attention Streams. RSS readers and aggregators that notice Attention Streams can be used on the desktop, in Web enabled phones and other mobile devices and can be connected through a secure online attention data aggregator that ties users together so the whole community can benefit from aggregating, triangulating and filtering Attention Streams.

At the simplest level, using an RSS reader that intelligently notices Attention Streams on desktop PCs can get the pruning process started. By intelligently analyzing both obvious and the subtle Attention Streams, articles that are more likely to be of interest can be prioritized and brought the forefront. Less interesting articles can be pruned or stored for browsing when and if time permits.

Connecting desktop and mobile devices with an online aggregator has the additional advantage of making articles available where ever the user happens to be. Elegantly synchronizing multiple devices at the article level gets rid of duplicates and eliminates the time sink of having to clean up the same subscriptions from multiple access points. And it keeps feeds and articles organized and prioritized no matter where or when they are accessed.

The real advantage of connecting Attention Streams in an online community is driven by the mysterious “Wisdom of Crowds”. By providing a secure, permission based, privacy protected environment where Attention Streams can be captured, recovered and shared, it’s possible to discover feeds and, more importantly, specific articles that friends, collogues and affinity groups are paying attention to. With the right processing techniques this can be done in near real-time to provide up-to-the-minute flow of highly relevant information that delivers on the promise that less is more.

Marc Orchant (describes his use model for reading RSS as a “palette cleansing sorbet - finish a task, sample some feeds and then move on to the next task.” Intelligently using Attention Streams can enliven the menu with “appetizers” of recommended new content from people who share an affinity with the subjects that interest you. These appetizers can lead to those wonderful accidental learning experiences that give us the all too rare ah ha moment.

-e