> For the complete documentation index, see [llms.txt](https://docs.insoblokai.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.insoblokai.io/whitepaper/user-engagement-and-social-mechanisms/algorithmic-model.md).

# Algorithmic Model

<figure><img src="/files/03AFEi8Ddn3SKLFG7G01" alt=""><figcaption></figcaption></figure>

The TasteScore engine is powered by a **multi-factor algorithm** that synthesizes:

* **Behavioral input signals** (e.g., voting history, engagement velocity)
* **Social graph analytics** (e.g., interactions with high-TasteScore accounts)
* **Contextual content evaluation** (e.g., fashion quality, uniqueness, remix lineage)
* **AI feedback loops** (e.g., generative AI interpreting style coherence or sentiment)

TasteScore is calculated using a **weighted composite model**:

TasteScore = f(Engagement \* Style Quality \* Social Trust \* Influence Spread)

Each variable is adjusted in real-time via machine learning models that ingest **on-chain events**, **AI-enhanced metadata**, and **cross-account interaction patterns**.