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AI dating simulator and companion app data

verify

Open data behind Awesome AI Dating Simulators.

One row per product across iOS, Android and web. For each product: the platforms it ships on, the relationship mechanics its own store listing documents (with the sentence quoted), store ratings with capture dates, and a six-signal score that score.py recomputes from the published inputs.

Snapshot 2026-09-18

Measure Value
Products 87 (7 dating simulators, 23 companions, 57 character chat platforms)
Platform coverage 71 on iOS, 58 on Android, 28 on the web. 18 products ship on all three, 34 on two, 35 on one.
Mechanic flags 8 per product, 696 cells, 38 true. 28 products document at least one mechanic in their listing.
Scored products 48 (App Store products with ratings in at least 10 of 24 storefronts)
Capture window App Store data 2026-09-11 to 2026-09-17, platform and web checks 2026-09-18

Files

File Rows What it is
apps.csv, apps.json 87 Category, platforms, store and web URLs, developer, eight mechanic flags with the listing sentence for each, pricing, ratings.
inputs.csv 48 The raw inputs behind the score: weighted rating, rating count, newest-review average and count, cohort median, integrity penalty, days since update, feature checks, quality codes.
scores.csv 48 Output of score.py: six signals and their mean, sorted by score.
score.py Recomputes scores.csv from inputs.csv. Python 3 standard library, no dependencies.
METHOD.md Every formula, every flag definition, storefront list, exclusions.

Reproduce

python3 score.py
git diff --exit-code scores.csv

The second command exits 0 when the recomputed file is byte-identical to the committed one.

How to read apps.csv

  • category is simulator, companion or character_chat. Definitions and the assignment rule are in METHOD.md.
  • platforms joins ios, android and web with a semicolon character. A platform is listed only when the store page or web app was opened on the check date. Platform count enters no score.
  • The mechanic columns meter, message_scoring, ghost, fail_state, progression, proactive, availability, memory are true only when the product's own listing states the mechanic. notes quotes the sentence. false means the listing does not describe it, which is not the same as the product lacking it.
  • ios_rating is the rating-count-weighted average across the 24 storefronts. android_rating and android_installs are read from the US Google Play page.
  • last_checked is the date the row was verified.

What this dataset does not measure

  • Behaviour in use. Every flag comes from listing text, not from running the product.
  • Google Play ratings outside the US storefront.
  • Products whose main draw is explicit content. They are excluded by scope, not scored low.
  • Web-only products have no store ratings and are therefore unscored.

Update cadence

Store data is refreshed monthly and last_checked on every row says when. Mechanic flags change only when a listing changes. A pull request that flips a flag must add the new listing sentence to notes.

License

CC0 1.0. Use the data and cite the repo if you can.

About

Open data behind Awesome AI Dating Simulators: platforms, documented mechanics with listing quotes, store ratings, recomputable score

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