Nostr Web Client

GrapeRank uses something called "interpretation" to translate whatever input you have access to & find valuable into a standardized format that is ready for digestion by the GrapeRank algo. GrapeRank is also contextual. So far, I have implemented only one GrapeRank context: verified real nostr user. And yes, the input is follows and mutes. Other GrapeRank contexts will take other inputs.

But when I say PageRank is about popularity, I'm not referring to the input. I'm referring to the score itself. It's designed to be a measure of popularity. The more followers you attract, the higher your PageRank score gets. It incentivizes you to become an Influencoor.

The GrapeRank "real nostr user" score is not a popularity contest because your score does not continue to grow unbounded. Instead, it levels out at unity. If you have 0 followers, your score is 0. And then it increases with each follower, depending on that follower's GrapeRank score. If you get 50 high quality followers, your score might be something like 0.95. If you boost your follower count to 500, your GrapeRank score may be something like 0.995. For this particular GrapeRank context, your score never goes above 1.

Now, I'm not saying we shouldn't use PageRank. It's great for filtering out spam and it's (relatively) easy to calculate. It's well-known and there are lots of relevant tools out there. I use neo4j to calculate PageRank as one of three WoT scores currently available on my site. But it's not the not the final word in graph-based recommendation engines. It's barely the first word. A popularity score makes sense for Google, bc influencers drive traffic and increase ad revenue. For freedom tech, we need to think deeply and differently about how and why we use these algos.

franzap 11mo ago

I asked about inputs because that defines if it's usable or not.

What kind of interpretation is performed, and how exactly is that fed to the algo?

And also what is the goal of Graperank? Personalized Pagerank gives you a very good idea of the trusted people. You can then apply context, at least we are not suggesting it as an automatic curation tool.

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Discussion

david 11mo ago

Interpretation means take whatever data is available to you and translate it into a ratings format that is ready to be digested by the GrapeRank algo.

For example: if Alice follows Bob, I INTERPRET the follow AS IF she had issued a rating in the GrapeRank format. Which she didn’t, of course, but that’s why I call it “interpretation.”

The format requires 5 fields:

- context (string)

- rater (string)

- ratee (string)

- rating (number)

- confidence (number between 0 and 1)

At my site right now, every follow and every mute is INTERPRETED as a rating, issued by one pubkey to another pubkey. The context is something like: Real Nostr User. The rating field is a 1 or 0 for follow or mute, respectively. The confidence is 0.03 or 0.5 for a follow or a mute, respectively.

The final GR Real Nostr User influence score is a number that is suitable to be a weight in a weighted average (eg, to calculate ratings at Yelpstr if such an app were to exist). It is a number between 0 and 1, where 1 means “verified Real Nostr User.”

If we were to use PageRank to calculate average scores of businesses at Yelpstr, the opinions of the K Kardashians of the world would dominate. If we were to use the GrapeRank “Real Nostr User” score, the opinions of K Kardashian and A Einstein would carry roughly equal weight.

franzap 11mo ago

To your last point, not if we used Personalized Pagerank.

So PP in your model would be one application of GR.

In practical terms how do you foresee users choosing their contexts (free form or taxonomy?), ratings and confidence levels? Or will this mostly rely on interpretation?