This week’s featured collector is pairmike
Pairmike has a cute collection of pixelated pfps. Take a look at lazy.com/pairmike
Last week’s poll on Fake World Assets landed exactly where you’d expect a scarred-but-curious audience to land: 60% said “interesting, but not with my money.” The remaining votes split evenly between “owner-set rarity is genuinely new” and “glad to see an NFT experiment” at 20% each, while both the strong opinions — praise for the closed token launch and criticism of the lootbox odds — drew zero. That’s a coherent picture. Our readers engaged with FWA the way we framed it: as a mechanism-design curiosity worth understanding from a distance. Nobody was provoked into either defending or condemning the specific mechanics; the audience simply appreciated the novelty and kept their wallets closed. It’s also consistent with what this same readership told us during the NFTX coverage, when 29% said NFT-fi had burned them before. The appetite for watching experiments clearly exceeds the appetite for funding them — which, honestly, is probably the correct posture for a protocol whose price discovery hasn’t happened yet. The real test of sentiment comes when FWA’s buy gate opens and we see whether “interesting” ever converts to “invested.”
Teaching Machines to Recommend the Weird Stuff
Discovery is one of the quiet crises of the NFT space. When Foundation shut down in April, a quarter of our poll respondents named “discovery getting harder for artists” as their top concern — and as marketplaces consolidate, the question of how collectors actually find work becomes more urgent, not less. So it caught our attention that a new peer-reviewed paper published by IEEE takes on NFT recommendation systems directly, and specifically the part of the problem that matters most for art: the long tail.
Here’s the setup in plain terms. As Web3 platforms scale, NFT marketplaces increasingly need recommendation engines — the same way e-commerce sites suggest products you might like. Every NFT carries a rich set of labels: semantic, stylistic, thematic. A single piece might be tagged generative, monochrome, audiovisual, on-chain, and a dozen more things. That label space gets enormous fast, which is why researchers treat NFT recommendation as what’s called an extreme multi-label classification problem — predicting which of potentially thousands of labels apply to a given item and user.
The standard engineering solution is something called a probabilistic label tree, which recursively splits the giant label space into smaller chunks so the computation stays manageable. It works, but it has a bias problem that collectors will recognize instantly: label distribution is highly skewed. A handful of “head” labels — think popular categories like PFP or anime — appear constantly, while thousands of “tail” labels describing niche styles, obscure themes, and unusual formats appear rarely. Systems trained on this data get very good at recommending what’s already popular and very bad at surfacing the rare stuff. The algorithm, in other words, has the same bias as the market.
The paper’s contribution is a framework called ChainTail, built on a simple but clever observation: labels aren’t independent. They have inherent dependencies — certain styles co-occur with certain themes, certain formats cluster with certain aesthetics. ChainTail exploits those relationships in two ways. First, a dependency-aware partition module groups highly dependent labels into subsets when building the tree, so related rare labels support each other instead of getting scattered. Second, a dependency-aware re-scoring module re-ranks prediction scores to strip out label priors — essentially correcting for the popularity bias baked into the raw data. The experimental results show the approach measurably boosts tail label recommendation on widely used datasets.
Why should collectors care about the plumbing of recommendation systems? Because the tail is where the art lives. The head of the distribution is floor sweeps and blue chips; the tail is the experimental audiovisual work, the niche generative styles, the unclassifiable pieces this newsletter exists to talk about. If the discovery infrastructure of the next generation of marketplaces can only see the head, the market’s attention stays concentrated and the long tail of artists stays invisible — no matter how good the work is. Research that makes algorithms better at surfacing rare, weird, dependency-rich work is quietly pro-artist and pro-collector, even if nobody involved would put it that way.
There’s also a familiar echo here. We’ve covered how platform closures erase context and how curation keeps struggling to find a sustainable home. Recommendation systems are curation at scale, whether we like it or not — and the difference between an algorithm that amplifies what’s already popular and one that can genuinely explore the tail is, functionally, the difference between a market that discovers new artists and one that recycles the same fifty names. It’s worth knowing that serious researchers are working on the right side of that problem.
The honest caveat: this is early-stage academic work, tested on research datasets rather than deployed in a live marketplace, and there’s a long road between a published framework and a discovery feed you’d actually use. But the direction matters. The infrastructure conversations we cover usually happen at the protocol layer — this one is happening at the attention layer, which may matter just as much for what gets collected next.
This post is based on the paper introducing ChainTail, published by IEEE: https://ieeexplore.ieee.org/document/11331420
Poll: How do you discover new NFT art?
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