There has been a lot of buzz about a new breed of Attention based readers lately. Chris Abraham writes about Particls on Marketing Conversations and Jack Vinson writes about AideRSS here.

A great attention driven reader should make you feel like you need a tinfoil hat to protect you from its accurate mind reading powers. (Thank you Chris for perfect mental image).

I thought it might be helpful to take a deeper look into how these attention driven RSS readers differ in their approaches to finding relevant content. All of these tools are free so you can easily check them out and see for yourself which approach works best for your personal workflow.

AideRSS PostRank

AideRSS features PostRank???, a scoring system that ranks each article on relevance and reaction. AideRSS uses the explicit attention behaviors of others to rank feeds. The PostRank is a popularity contest ranking of explicit attention behaviors that takes into account the number of comments, bookmarks and trackbacks a given post receives. AideRSS depends on information from digg,, Technorati, IceRocket and Bloglines to work efficiently. These sites track the behavior of the most proactive readers who explicitly rate, comment and tag blog posts.

The challenge with this approach is that it focuses on the few to deliver results to the many. AideRSS gives you collective recommendation, not really a personalized recommendation. It ignores the lurkers and passive readers who may find highly relevant material but just file it away in their heads. This approach may also be prone to gaming and unchecked blog spam that can drastically impact results. More importantly it ignores your personal reading behavior which is ultimately the most significant indicator of what is personally relevant to you.

For PostRank to work it requires time to pass to allow for comments, trackbacks and bookmarks to accumulate. For time critical information this is a non-starter. In the enterprise getting the right information at the right time is the difference between success and failure. By the time the PostRank rating scales, you’ve may miss critical time sensitive information. It’s like getting yesterday’s news today.

There are some real advantage in this approach. If you can discount the time element, the PostRank technique is best suited for sites with lots of traffic and frequent posting covering content that isn’t time sensitive. You can turn the RSS spigot up and down to control the flow and it eliminates duplicate posts. Best of all you can subscribe to a single filtered feed with top ranked articles from all of your feeds. (Note: you can get the same effect with the Article Ranking view in Attensa without setting up a special feed).

If you flip this tool on its head, the true benefit in AideRSS may be its value to publishers as an excellent reporting tool.


I might be missing the big picture but in my analysis but here’s my take. Particls is a persistent search tool with smart filters requiring manual tweaking to optimize relevancy. Keywords are tracked and articles are displayed. I played with it and tried the keyword attention, Enterprise 2.0 and Attensa. After a few minutes articles starting show up in the sidebar that contained my search parameters. Posts on Attensa and Enterprise 2.0 were relevant but not complete. My search on attention produced articles on every topic under the sun with the word attention in the headline. On the plus side, there weren’t any duplicates, which is a flaw in the current persistent search technique used by Attensa.

Particls is more of an intention capture tool than an attention tool. For Particls to be effective it requires constant attention (no pun intended). You create and tweak smart filters manually to improve results. From my own use I couldn’t really discern any automatic ranking.

The Particls river-of-news sidebar is a paradox. The tool that is supposed to focus your attention ends up being a distraction. Articles flow by and if you snooze you lose. Articles are difficult to organize. In my own workflow I like to keep by feeds organized by subject and project and check out the articles when it best fits my schedule. I use alerts for time critical information.

Attensa AttentionStream

Attensa uses an AttentionStream based on machine learning techniques to bubble-up the most relevant content from all of your feeds to the surface. The AttentionStream technology behind Attensa’s Article Ranking View of your feeds combines content cues (keyword, search terms, authors, titles, tags and more) with your personal explicit and implicit attention behaviors to provide a relevancy ranking. The beauty in this approach is you don’t have to do anything special to get optimized results.

Attensa???s predictive ranking AttentionStream??? technology continuously observes and analyzes explicit and implicit behavior as you read and process feeds and articles. By continuously analyzing AttentionStream??? data, including the time and frequency that feeds are accessed and the number of articles read, deleted and ignored, RSS articles can be displayed in a prioritized list based on the likelihood that they will be of interest to you. Feed and article priorities are constantly refined as the continuous stream of attention is processed. You don’t have to change your behavior to get the most benefit. And, it’s personalized attention – not group think. No tweaking, no adjusting, the inscrutable Attention engine works in the background continuously optimizing your ranking in real time. For the enterprise these behaviors can be tracked through metrics and reports that can be used to identify the most efficient communication channels for getting the right information to the right people and internal publishers can get reports on how their content is being consumed

The Attensa approach isn’t perfect but the important stuff does have a way of coming to the surface.