Event tracking refers to recording user interactions alongside search results. This is usually the successful search outcome customers use to power machine learning.
Eventtracking tokens are not required, and events are simply bound to the query that generated the result set and a unique identifier for your result (i.e. the name of a field in your schema with the Unique constraint).
Using data generated from tracking clicks on results and any other events your app sends back to our API, our machine learning can update relevance scores and improve results shown to users.
Click tracking refers to recording user click interactions alongside search results. This is usually the successful search outcome customers use to power machine learning.
Clicktoken records an interaction for the query-result pair when an end-user enters a query and clicks on a search result.
Using data generated from click tracking, machine learning can update relevance scores and improve results.
Note: These instructions are for testing "Search Interface Builder 1.4" using Google Chrome.
- 1.To test click tracking, on a search results page in a browser, open the developer tools by right-clicking and choosing "Inspect" or
CTRL + SHIFT + I.
- 2.Navigate to the "Network" section and click the checkbox to "Preserve log".
- 3.Enter a new search query in the interface, and then press enter. Once the results have rendered, press "Clear" (next to the red recording button) to make it easier to find the click request.
- 4.Click on any search result to navigate to the result destination page and list all the requests that the browser made while fetching the page.
- 5.In the Network tab, search for "re.search.io/token" among the request Headers.The request will be a hashed code with the Request URL beginning
https://re.search.io/token/When this is present, a token has been generated and recorded.
PosNeg tracking refers to the recording of variably weighted user interactions with search results. As the name suggests, PosNeg tracking can be used to record both positive and negative interactions on query results. The resulting data is used for analytics and to power machine learning.
PosNegtracking, both pos and neg tokens are generated for query-result pairs. Your app should store the tokens the user interacts with in ways you find relevant, e.g. a like, dislike, click, vote, product view, cart add or purchase. When these interaction events occur, your app should then send them back to our API along with the name of the event and optionally a weight to indicate strength when you have events of differing importance.
PosNeg tracking corrects for position bias by:
- 1.penalizing negatively interacted results
- 2.penalizing results occurring above those with positive interactions that were not interacted with
- 3.boosting results with positive interactions
- clicks are not a good measure of success
- multiple different events with different values that optimize differently (e.g. cart adds, purchases, wishlists)
- implicit negative interactions occur (e.g. tinder-style thumbs up/down)