Monthly Archives: April 2010

Graph Attention Profiles – GAP(ML)

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This was an idea I had earlier this morning about how to optimize social ad placement services, (MyLikes (Aff. Link), Magpie, etc. ) These services work by placing ads into the a social stream , I like MyLikes model, they let you decide what to put into the stream based on what you like, but this doesn’t factor in what your followers like, the ad needs to be relative to them, not you*. Thinking about how to determine the relevancy to a group, I came up with an idea based around averaging individual APMLs(Attention Profiling Mark-up Language).

I haven’t thought it out fully, it’s only been a few hours, but using APMLs as the starting ground. You sum the weights, per topic, for all of your followers and then divide by #number of followers, to get the APML for your Social Graph, per network which I’m calling GAP currently. I see this as an extended OPML format for APMLs , handling not only weights of relevant interest, but also handling access to the APMLs monitored by the graph.

One thing that would conflict with the APML format, which the GAP could stay very close to, is what is deemed Explicit Data. You aren’t the one determining relevancy, so it isn’t necessary. I’d either use or replace it for something that handles the APML list being monitored, the list becomes the explicit data for the weighting, but it also allows you to weight the APML’s individually as well, I don’t know that this is necessary, but it allows accessibility to possibly increase relevance to your graph, based on who is likely to interact more with you.

So this is just a thought, about a open-method for sharing graphs and relevance between services, rather than every service handling a proprietary model of the graph, and a proprietary model of relevant data. First things first, is that we need support for APML, which we have Chris Saad to thank for, then we can handle how we manage our networks relevancy.

One final issue with the GAP is that it has a specific use case, is that it is a way to share graphs and relevancy to exterior networks, but the file size for the GAP if it handled all the networks simultaneously it would become quite large, implicit data would be 1 line per topic, per network, and explicit data would be 1 line per person, per network. For early adopters and people with large following bases this could become quite large, even for a regular user on one network it would likely be 300-1000 lines.

*= MyLikes already uses a similar model, influenced by clicks per ad and number of ads you share. MyLikes Influence Rank

Hate & Resignation

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It is only out of ignorance that people are cruel, because they really don’t think it will come back. ~ Maya Angelou

This quote, is extremely fitting to this tale. Recently, I saw an indirect, independent action that could be seen as a return of karmic justice, not because I was particularly cruel, but because I was ignorant at a prior time. For many of Friendfeed’s more active English speakers, they may remember the racial slur topic, which cost the service some of it’s best intellectuals. Sadly, I must say that I was one of the “very few” people who kept stoking the fire, long past the point of which it should have been ash; I stoked that fire, and the results of the blaze is some people were singed, but a few were charred.

Do I have regrets for being involved? Absolutely.
Do I think I had reason for being involved? To a point, but I stepped far past what that was.
Was I cognizant of all the rammifications as I made my decisions? No, and I will admit many of those remarks were made in defense, though an unreightous defense, to what  I though were attacks to  my and others credibility.

One thing that I did not completely understand, though I thought I did, was the power of words. I bore that if they were used in good fun and jest, that they shouldn’t have such intensity, the ability to cause pain. Well all that changed last weekend, when I felt attacked directly, by words that I was likely not to see, but happened upon them anyways.

These words may not have been directed upon me, but they were forged from an intense hatred, and were likely set off by something I said haphazardly and in a way mocking my own ignorance. These words not only included that of hatred, but those of compassion, which seemed very cold and insincere when they were breached, and sarcastic thoughts of violence. I can now say that I was, or at least feel as though I was, on the recieving end of a racial hate crime, even if it was only words, from thousands of miles away.

I can also say that to say that you understand, to say that you have compassion, after supporting or standing up for any form of bigotry, even if it was only words spoke in jest, will be called into question. You should not fight when this question is raised, because you, too, are part of the problem. You will stand along side me; you will wait until that suffering returns, to show you its effects. We all need to remember that there is a line, but we must also realize that what lies on either side is the same.

Sadly, it is with this that I have also chosen to step away from friends, all of whom I’ve shared great moments with, and who will share great moments without me. I may stop to observe how they are doing, but my interaction, has caused quite huge losses, not just for myself, but for them as well. I feel now it is better for me to resign, as a member of FF, of course I still reserve a position to be involved with the argumenting about domo’s and the “fat kid.”

An Antithetical Post On How Narrowing Is The Key to Curated Data

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So this whole thing about curation , has my head in a state, where I am seeing the data, meta-data, and users, as distinct entities in three-dimensional space. I’d love to provide an image of how they are related, but I can’t because when it comes to placing them in a 2-D or even 3-D state, there is warping and tunneling between these objects, outside of the third-dimension, to maintain proper relations.

Still here? Good. This post may be a bit vague, I’m going to try and keep it simple and understandable, for you as well as myself, I’m already a bit confused after several hours of trying to map this. If you would like to discuss this, for a more in depth, though possibly less coherent form, feel free.

To begin, we have three entities: data, meta-data, and users. These entities all have various ranges of relationship, which go from near to distant, and occasionally don’t exist. To describe the range as an example of friends, “Those best-friends, with very similar taste, are near(1), friends, much different taste(2), acquaintances, similar taste(3), acquaintances, different taste(4), and people you’ve never met(0).” We’ll approach range using this method, based on relational distance, between entities.

Data is, in my view, the front facing objects, whether that be text, images, video, or even tactile objects. Data itself exists in a weak presence, as far as to what value it represents, when coupled with meta-data, it becomes stronger.

Meta-data is data about data. It is the entity that is manipulated and understood, to provide us with relationship information, on any level. There are many forms of meta-data, temporal, location, authorship, topics, etc., that provide us with fantastic ways of connecting data, but often times it includes disparate entities, that aren’t necessary.

The user in my case is a human which interprets the regular data, and may create tags of meta-data, but can be a machine in which case it is likely to work with meta-data, either directly or in composition of meta-data from data sources.

Now that the entities are somewhat defined, I can get into the discussion of how these various entities are connected in creating relevant connections, both in basic terms, and user specific terms.

Often times, the simplest way to construct a relevancy map between data objects, is to use meta-data about the objects, social-bookmarking tools work this way by way of topical tagging, the distance between objects is the range of 4. Making the system a bit more complex you add methods, you take your tagged set, and add in user selection, by how much a user likes various items to manipulate what topics they are likely to see, this is in the range of 3 because it is still picking out items by topic which is a very wide. Or you can provide what your user’s friends have read recently, this is still in the range of 3, because by adding in what other people read, can narrow the area of focus, it’s possible to be in areas that the user doesn’t care as much for. If you add in what the user’s friends like, rather than just what they read, you get closer to the range of 2.

In order to get to the optimal range 1 you have to add two more things to your system: direct relations between data-objects and concentrated interaction between users, these can both be defined explicitly by users, and can be shown as a simple social-graph, with one object/user in the center, and the closest elements near by.  Direct-relations, which are somewhat like Techmeme, can be created on a broad scale by a user-based system of bundling links to content, based on relationship. Concentrated Interaction is a bit more complex, because it requires an analysis of interaction, but presents an interesting system, helps reach the range of 1.

Note: If you treat Users like data-objects, which they are in a database, you can apply meta-data, to make the concentrated interaction, more specific by what topics the user is most familiar.

So I’ve discussed 5 ways in varying levels of implementation to reduce the range of relevancy.

The use of tagging to create a quick reduction in the range of relevant data.
User selection to narrow down what topics the user likes, or aggregate content that the users friends are looking at.
Further narrow it down by what these friends like.
Allow Bundling of content that is directly related.
Analyze the concentrated interaction graph to narrow down trust sources.

I’m sure I’ve lost someone in this antithetical pile, as I had to get this off my head it was driving me crazy, and I’m going to call it the beginning of a new arcling, to be adjusted down the line. So if  you are interested, I’m sure that we can possibly make it a bit clearer by having a discussion.