# Recommender system
## MicroSim spec
### Parameters (tunable controls)
- `Users` · 2–30 · users on one side of the bipartite graph
- `Items` · 2–30 · items on the other side
- `Similarity threshold` · 0–1 · cutoff for recommending a neighbour's item
### What animates
A bipartite user-item graph draws recommendation edges above a similarity threshold.
### Learning objective
Show how user-item similarity drives recommendations in a bipartite graph.
## Links (Wikipedia order)
<!-- injected from _registry/childlinks/Recommender_system.json (2026-07-30T02:09:12Z) -->
`A/B_testing` · `ACM_Conference_on_Recommender_Systems` · `Academic_journal` · `Accuracy_barrier` · `Alexander_Tuzhilin` · `Algorithm_(disambiguation)` · `Algorithmic_radicalization` · [[Artificial_intelligence]] · `Association_for_Computing_Machinery` · `Attention_(machine_learning)` · [[Black_box]] · `Cardinality` · `Click-through_rate` · `Cluster_analysis` · `Cold_start_(computing)` · `Cold_start_(recommender_systems)` · `Collaborative_filtering` · `Collaborative_search_engine` · [[Collective_intelligence]] · `Computing_platform` · `Configurator` · `Content_moderation` · `Cosine_similarity` · `Customer_review` · `Decision_support_system` · `Deep_learning` · `Dimensionality_reduction` · `Dot_product` · `E-commerce` · `Effectiveness` · `Elaine_Rich` · `Enterprise_bookmarking` · `Evaluation` · `Federal_Trade_Commission` · `Filter_bubble` · `Foundation_model` · `GitHub` · `Gonzalez_v._Google_LLC` · `Gravity_R&D` · `GroupLens_Research` · `IEEE_Spectrum` · `IMDb` · `Implicit_data_collection` · `Information_explosion` · `Information_filtering_system` · `Information_privacy` · `Information_retrieval` · `Item-item_collaborative_filtering` · `Joseph_A._Konstan` · `Journal_of_Big_Data` · `Journal_of_Medical_Internet_Research` · `Jussi_Karlgren` · `K-nearest_neighbors_algorithm` · `K.G.M._v._Meta_et_al.` · `Knowledge-based_systems` · `Knowledge_base` · `Knowledge_engineering` · `Last.fm` · `Latent_Dirichlet_allocation` · `Latent_semantic_analysis` · `Long_tail` · `Lyft` · `Lyle_Ungar` · [[Machine_learning]] · `Matrix_factorization_(recommender_systems)` · `Mean_squared_error` · `Media_monitoring_service` · `Metadata` · `Mobile_device` · `MovieLens` · `Multi-armed_bandit` · `Multimodal_sentiment_analysis` · `Music_Genome_Project` · `Naive_Bayes_classifier` · `Natural_language_processing` · `Netflix` · `Netflix_Prize` · [[Neural_network_(machine_learning)]] · `New_York_City` · `Online_dating` · `Pattern_recognition` · `Pattie_Maes` · `Paul_Resnick` · `Personalization` · `Personalized_marketing` · `Personalized_search` · `Peter_Brusilovsky` · `Playlist` · `Precision_and_recall` · `Prediction` · `Preference_elicitation` · `Product_finder` · `Profiling_(information_science)` · `Raymond_J._Mooney` · `Recurrent_neural_network` · [[Reinforcement_learning]] · [[Relevance]] · `Reputation_management` · `Reputation_system` · `Search_algorithm` · `Sentiment_analysis` · `Seq2seq` · `Serendipity` · `Similarity_search` · `Singular_value_decomposition` · `Smartphone` · `Social_media` · `Social_networking_service` · `Software` · `Special_Interest_Group_on_Information_Retrieval` · `Star_(classification)` · `Statistical_distance` · `Supervised_learning` · `Text_mining` · `Tf–idf` · `Uber` · `User_profile` · `Vector_space` · `Video_Privacy_Protection_Act` · [[Wayback_Machine]] · `Website` · `YouTube`
## From the Real GENERATIVE library
*Animated: Recommender system — placed from the Real G.E.N.E.R.A.T.I.V.E. course library (Engineering room). Source: Wikimedia Commons (via Wikipedia article media). [Details & license](https://commons.wikimedia.org/wiki/File:Collaborative_filtering.gif).*
> A recommender system, or a recommendation system (sometimes replacing system with terms such as platform, engine, or algorithm), is a subclass of information filtering system that provides suggestions for items that are most pertinent to a particular user.[1][2][3] Recommender systems are particularly useful when an individual needs to choose an item from a ([Wikipedia](https://en.wikipedia.org/wiki/Recommender_system))
<!-- REAL-GENERATIVE-MEDIA:END -->
> p5.js MicroSim stub · part of Systems Engineering · [Wikipedia source](https://en.wikipedia.org/wiki/Recommender_system)
## Concept summary
A recommender system, also called a recommendation [[Algorithm|algorithm]], recommendation engine, or recommendation platform, is a type of [[Information|information]] filtering [[System|system]] that suggests items most relevant to a particular user. The value of these systems becomes particularly evident in scenarios where users must select from a large number of options, such as products, media, or content.
## Build checklist
- [ ] Claim it: set `status: in-progress` + `lease` + `leased_at`
- [ ] Write the child page explaining the concept (tie it back to Systems Engineering)
- [ ] Finalize parameter ranges and defaults
- [ ] Implement the p5.js sketch (drawing + control regions)
- [ ] Add caption + the learning objective on the page
- [ ] Set `status: done`
## p5.js sketch
```javascript
// MicroSim: Recommender system
const ARTICLE = "Recommender_system";
let p1, p2, p3;
function setup() {
createCanvas(720, 520);
pixelDensity(2);
// network-graph sim — create the three sliders described above:
// Users (2–30), Items (2–30), Similarity threshold (0–1).
// TODO: build labelled sliders in the control region (below the drawing region).
}
function draw() {
background(248);
// TODO: read p1/p2/p3; render the network-graph in the drawing region and respond live.
// TODO: HUD — title + en.wikitube.io/wiki/Recommender_system + control hints + live readouts.
}
```
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Back to Systems Engineering
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## Semiotic profile
> *The semiotic universals this article invokes, machine-derived from the crossref — **unverified** (born so). Populated 2026-07-06 for the Systems room.*
**Universals (5):** 🟡 system (11) · 🟢 network (9) · 🟢 threshold (3) · 🟡 information (2) · 🟢 filter (1)
**Enter by sign:** Systems Semiotic Gateway · Alphabetum · Icon Registry · ← Systems Portal
<!-- SEMIOTIC-PROFILE:END -->
<!-- REAL-GENERATIVE-MEDIA:START -->
## Wikipedia : Wikitube
**Strict pair:** [Wikipedia](https://en.wikipedia.org/wiki/Recommender_system) : [Wikitube](https://en.wikitube.io/wiki/Recommender_system)
## Previous hub tags
Tree parents: [[Control_theory]] · [[Cybernetics]] · [[Operations_research]] · [[Reliability_engineering]] · [[System_dynamics]] · [[Systems_engineering]] · [[Systems_science]] · [[Systems_theory]].
Legacy hubs: none.
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*Sources: 1 legacy note. Minted wave 1, 2026-07-30 (v1.6 order).*