Recommendations now update in near real-time
Established algorithms like Collaborative Filtering recommend content based on the behavior data of all your users. This way, updating the recommendations for a single user requires too much time and processing power to keep up with their current actions. The easy solution is to update recommendations periodically for all users, but this leaves them lagging way behind. Now, the latest version of our API can recommend content to your users based on their most recent activity. To use this new feature, add the parameter *engine=instant* to your API calls:
While this technique may be somewhat less accurate, recommendations do update in near real time. Combining these instant recommendations with periodically calculated ones gives you the best of both worlds.