Markov

A markov kernel for locally storing and encoding user preference as they navigate a higher-dimensional embedding space. It provides a local, portable point-of-view in an embedding space.

It has zero host dependencies and operates agnostically of the underlying embedding model.

The idea

Your "taste" and the content live in the same space, so we can think of preference as a measure within a measure space. The Markov kernel tracks a position, a direction of travel, and a spread as you interact with content in a given embedding space:

| | | |---|---| | mu | where you are - a recency-weighted mean of what you liked | | v | where you're heading - EMA of displacement between likes | | sigma | how wide to cast - grows on passes, shrinks on likes | | facets | leaky accumulators over tags, in whatever channels you name | | groups | signed EMA per group (artist, developer, publisher…): +1 liked, −1 passed | | scalarPref | a preferred value of one positive quantity (a length, a price), unit-free | | neg / blacklist | permanent suppression of a group | | muted | session-scoped fatigue, never persisted |

queryVector(state) returns mu pushed along v — you search slightly ahead of where you are, which is what makes it feel like motion rather than a filter.

mu is a bias-corrected EMA: the first like is the position, and later likes settle to weight alpha. Velocity starts at the second like, since one position has no direction.

Passes are disambiguated. Passing on a group you have a positive history with is read as fatigue (mute for the session, preference preserved); passing on one you don't is read as dislike (accumulates toward suppression). Conflating those two is why most recommenders punish you for your own favourites.

Debt owed to Gärdenfors, The Geometry of Meaning — this is a conceptual space with a walker in it.

Use

import { emptyKernel, onLike, onPass, queryVector, reweight } from '@fangorn-network/markov'

let k = emptyKernel({ d: 768 })
k = onLike(k, { embedding })                        // only the embedding is required
k = onLike(k, { embedding, group: 'nolla-games', facets: { genre: ['roguelike'] }, scalar: 12 })
k = onPass(k, { embedding: other })
const hits = await db.search(queryVector(k))        // [{ id, embedding, distance, metadata?: { group, facets, scalar } }]
const ranked = reweight(hits, k).sort((a, b) => b.weight - a.weight)

Every optional field adds one signal when it is there and is ignored when it is not, so a caller with nothing but vectors gets the geometric kernel alone.

serializeState / deserializeState round-trip it to JSON. It is a small file on your disk. Nobody else has a copy.

Check

npm test          # node test/kernel.test.ts — no deps, Node 22.18+
npm run build     # dist/ — the JS that ships (Node will not strip types under node_modules)

Five assertions, run in two spaces: liking leans the query, ranking follows the lean, passing pushes away, state survives serialisation, and — the interesting one — a profile built only in catalogue A correctly ranks an unseen catalogue B sharing the space. Current margin on synthetic vectors: 4.1x. Then the general cases: bare vectors, fatigue vs dislike by group, a unit-free scalar, caller-named facet channels, and state saved by 0.0.x still loading.

ok  d=384  lean=0.983  pass=0.0074->0.0061  transfer=4.14x
ok  d=768 (canonical)  lean=0.983  pass=0.0074->0.0061  transfer=4.14x
5 checks passed in each space
ok  general: first like is the position, bare vectors, fatigue vs dislike by group, unit-free scalar, caller facets, 0.0.x state loads

The space

The canonical space is nomic-embed-text-v1.5, 768 dims, chosen because it is Matryoshka: the same vectors truncate to 512/256/128 for centroids and coarse scans, which is what lets a catalogue advertize its coverage in kilobytes. It is Apache-2.0, has ONNX weights for in-browser use, and is English-first — that last one is the known exposure.

A kernel is only meaningful inside one space. params.d sets the dimension and must match the catalogue's declared embedding.dim; two catalogues on different models are different spaces, and moving a profile between them needs a learned projection that does not exist yet.

Known limits

• No projection between spaces. A profile does not survive a change of model.
• The transfer result above is synthetic — clean, well-separated regions. It proves the mechanism, not that real cross-domain taste is legible.