Graph Attention Profiles – GAP(ML)

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