tubestation/docs/rust-components/topic-guides/relevancy.md

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Relevancy

The relevancy component tracks the user's interests locally, without sharing any data over the network. The component currently supports building an interest vector based on the URLs they visit.

Setting up the store

To use the RelevancyStore in either Kotlin or Swift, you need to import the relevant classes and data types from the MozillaAppServices library.

:::{tab-set-code}

import mozilla.appservices.relevancy.RelevancyStore
import mozilla.appservices.relevancy.InterestVector

val store = RelevancyStore(dbPath)

import MozillaAppServices

let store = RelevancyStore(dbPath: "path/to/database")

:::

To work with the RelevancyStore, you need to create an instance using a database path where the user’s interest data will be stored:

:::{tab-set-code}

val store = RelevancyStore(dbPath)

let store = RelevancyStore(dbPath: "path/to/database")

:::

• dbPath: This is the path to the SQLite database where the relevancy data is stored. The initialization is non-blocking, and the database is opened lazily.

Ingesting relevancy data

To build the user's interest vector, call the ingest function with a list of URLs ranked by frequency. This method downloads the interest data, classifies the user's top URLs, and builds the interest vector. This process may take time and should only be called from a worker thread.

Example usage of ingest:

:::{tab-set-code}

val topUrlsByFrequency = listOf("https://example.com", "https://another-example.com")
val interestVector = store.ingest(topUrlsByFrequency)

let topUrlsByFrequency = ["https://example.com", "https://another-example.com"]
let interestVector = store.ingest(topUrlsByFrequency)

:::

• topUrlsByFrequency: A list of URLs ranked by how often and recently the user has visited them. This data is used to build the user's interest vector.
• The ingest function returns an InterestVector, which contains the user's interest levels for different tracked categories.