In today’s world consumers are overwhelmed by choice. LimeSpot solves the choice-overload problem by providing fewer choices but ones that are highly relevant and personalized for each individual consumer.

The engine behind personalizer.io is powered by advanced machine learning algorithms combined with linguistic analysis. The engine utilizes collaborative filtering concepts to filter and order the results for each individual user based on collective user behaviors. Most importantly, it is capable of analyzing and understanding the content being personalized and also the user profile of the individual to whom personalization is being delivered.

The end result is that consumers will see the content that they are most likely to be interested in.

personalizer.io provides an open API platform to serve personalization to businesses of any size. The technology is:

industry independent;
content independent; and
channel independent.
Before diving deeper, there are a few concepts we would like to familiarize you with.

How it works? To add personalization to your applications, there are two sides to the API implementation: Back-end and Front-end. We designed the APIs in a way that they are instantly applicable to your front-end use-cases, meaning that you don’t necessarily need to implement the back-end integration to be able to use this service. Simply log user activities, and this alone will let you recieve behavior-based recommendations (i.e. Top Picks, Recent Views, Trending, Popular, Related).
However, the behavior-based recommendations are only accurate when you process huge amounts of user behavior data, which is not available in many cases. If you are just launching a new website or app that doesn’t have many users and/or traffic this effect may be particularly pronounced. You don’t need to worry, though, if you don’t have a big user-behavior base.
Feeding the engine with details about your items (e.g. Products, Articles, etc.) and users profiles (i.e. Demographics, Interests) trains it to understand them better, and makes it smarter. This is where the magic of personalizer.io comes into play! It can deliver highly effective and targeted personalization to you, simply by learnig details about your items and users.

What is Identifier? In various methods when you are submitting or retrieving data (e.g. Items, Collections, Users) from endpoints, you will encounter a field called Idenfitier. This is basically the unique identifier in your system for that particular record. For example when you are submitting an ItemView activity to AcvitityLogs, the referenceIdentifier is the unique identifier of that item within your system. Then when you retrieve recommendations (e.g. Trending) from the Trending endpoint, it will return to you a list of Identifiers that should match with your own system’s IDs.

For complete documentation see personalizer.io