Personalized recommender systems provide online retailers with product recommendations, all matched individually to the buying behavior of each registered customer.
Everybody who has shopped online knows how it works: After buying a specific item, product recommendations are made to promote further purchases. Basically, these recommendations are a clever idea. The problem with these recommendations is that they are unpersonalized, generated according mass preferences. Personalized recommender systems, on the other hand, make recommendations based on an individual’s past buying behavior and reflecting that customer’s taste.
The aim of such innovative recommendation models is to improve the accuracy of statistical calculations, to generate more suitable product suggestions and, in the long run, to reach the following goals:
- Higher customer satisfaction
- Higher customer loyalty
- Greater market share
- Increased sales