Slaves of the feed - This is not the realtime we've been looking for
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I think a system like Google Reader system might have some traction here, wherein friends can recommend items to each other. With enough friends, you could make a metafeed out of those items. Make it a new service; you get paid for drinking from the firehose, and you pay to get human-filtered feeds. Throw some 'liked/hated this firehose drinker' and a pile of people at it, and you get interest spaces.
You don't need to drink from a firehose. The firehose will be throttled and filtered to given you the useful information that you want. This is where the future of real-time search lies.
Disclosure: I'm CTO of Collecta, one of these real-time search companies.
Surely filtering the "fire hose" with the query "programming news" will not yield satisfactory results.
Here's what'd help me with the bottleneck: a unified inbox with a wicked smart relevance algorithm. VR? Probably not.
I'd like to have a treemap view of google reader rather than a list, and where I could visually, in real time filter the feeds by adding negative and positive keywords/tags, and click around in various levels of a treemap.
Click on "technology" => make a new treemap of significant tags in that area, click on the next tag to dive into that space etc, and eventually see all the feed items somehow. Perhaps present them in all levels by some sidebar or hoverthing. Color code items for popularity / activity / freshness.
I'm thinking something like dabble.db but with a treemap interface and cappuchino js interface.
Another problem to solve with current news reading is duplication. Even if you do want to read about Tiger Woods, there's a lot of duplication in the echo chamber of the internet.
An interesting sidenote, apparently google could activate face recognition for google goggles but have chosen not to do that at this moment for privacy issues.
Just drop the link, some tags, and misc attributes along with a short description.. Is it much more complicated than that?
I agree. The problem is definitely a matter of related vs. unrelated content. My suggestion (or babble) was more so just pointing out how easily we could implement a "source content filtering" logic into an already existing interface.. If we felt that was the best way to go about it.
“This is how it should be done: Lodge yourself on a stratum, experiment with the opportunities it offers, find an advantageous place on it, find potential movements of deterritorialization, possible lines of flight, experience them, produce flow conjunctions here and there, try out continuums of intensities segment by segment, have a small plot of new land at all times. It is through a meticulous relation with the strata that one succeeds in freeing lines of flight...” – Deleuze and Guattari
When "relevancy" algorithms are attempted they don't do nearly as good a job at serving niche groups as "community" moderated algorithms such as HN.
HN is an algorithm, in which each person's brain is an equation that estimates the "worth" of piece of information. As a result the final product is an extremely complicated result of "equations", as it were, that are good at finding relevant information.
I don't expect computers to be good at doing that for another ten years or so.
A Naive Bayesian text classifier could easily learn that. If you fed it news stories that you are interested in it would quickly discover that you never click on stories about Tiger Woods.
It would then classify incoming news for you.
In fact, my old email project POPFile has been adapted to support things like RSS and NNTP filtering with ease.
I don't see why, in principle, the full Bayesian inference couldn't be done. In other words, automatically compute how interesting the item is based on how the masses liked it in aggregate, how individuals with similar tastes to you liked it, how similar the item is to other items that you have liked, etc.
I believe Reddit was at one point going to try and create a news recommendation engine but found it impractical as the number of users grew. The number of permutations for what people in just one user's network like and dislike is staggering.