Anitag2vec – Tag set embedding for ranking recommendations

Hacker News

I've been experimenting with using a tiny Transformer encoder without positional encoding a replacement for standard Deep Sets (permutation-invariant MLPs). The intuition: while Deep Sets are theoretically cleaner, a Transformer encoder naturally picks up on tag co-occurrence and spelling variations that sum-pooling layers often miss. I have trained this on anime/imageboard tags for now. Interestingly, even when testing on absolute nonsense/out-of-distribution tags, the model holds up fairly well. It seems to develop a strong sense of permutation invariance, with cosine scores remaining robust even when comparing sets to their subsets. Curious to hear if anyone else has experimented with stripping PEs from Transformers for set-based tasks.

Visit product View on Hacker NewsMar 25, 2026

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87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

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Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

31%31% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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