What is the Recommender System multi-solver?

Recommender System is a guided workflow that chains 2 individual PolySim solvers into one end-to-end analysis, piping each result into the next step.

Yes. Every step runs entirely in your browser using real numerics — no install, no account, no cloud cost. Custom or private solver packs are available as a paid service.

Data & Statistics Pack · Multi-Solver

Recommender System

Similarity & ranking. This multi-solver chains 2 solvers into a single guided workflow — run each step in order and carry the result forward.

Workflow steps

pagerank
→kmeans

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Pagerank

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PageRankLive

the algorithm that built Google

Controls

Presets

Damping factor d

PageRank ranks nodes by importance: a page is important if important pages link to it. It is computed by imagining a random surfer who follows links with probability d and jumps randomly otherwise, then finding where they spend the most time. This eigenvector of the link matrix launched Google and now ranks everything from proteins to social influence.

▶ Run in Python

Data Inspector

Damping0.85

Top node23.6%

Methodpower iteration

Governing equation

P R (p) = \frac{1 - 0.85}{6} + 0.85 q \sum \frac{P R ( q )}{L ( q )}

Reading this result: PageRank is the stationary distribution of a random surfer who follows links with probability d and teleports to a random page with probability 1−d. At d=0.85 link structure dominates but teleport still keeps every node reachable.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

↓

Kmeans

open full solver →

k-Means ClusteringLive

Lloyd's algorithm · live

Controls

Speed1×

Presets

k (centroids)

True clusters

Lloyd's algorithm: assign each point to its nearest centroid, then move each centroid to the mean of its members. Repeat until stable. Try setting k different from the true cluster count.

▶ Run in Python

Data Inspector

Iteration0

Inertia0.00e+0

Governing equation

J = k = 1 \sum 4 x \in C_{k} \sum ∥ x - μ_{k} ∥^{2}, μ_{k} = \frac{1}{∣ C _{k} ∣} x \in C_{k} \sum x

Reading this result: k matches the 4 true clusters, so each centroid can settle onto one real group and inertia falls to a clean minimum.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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More in the Data & Statistics pack

ML Model Trainer Classification Pipeline Clustering Workbench Feature Engineering Deep Net Playground Anomaly Detection Time-Series Forecaster NLP Basics Kit

Frequently asked questions

What is the Recommender System multi-solver?: Recommender System is a guided workflow that chains 2 individual PolySim solvers into one end-to-end analysis, piping each result into the next step.
Is it free to use?: Yes. Every step runs entirely in your browser using real numerics — no install, no account, no cloud cost. Custom or private solver packs are available as a paid service.