Recommendation Systems: Improving Buyers experience
Recommendation Systems offer customers products that they may have an interest in and aim at improving the purchase process They enable upselling and cross-selling. Among the most widely recommendation systems are: online stores, movies, videos, music, books, products or profile recommendations to follow in social networks.
The RS are based on a Machine Learning method which analyzes and processes users´ historical data (age, previous purchases, grading) of products (brands, models, price, content, similarity) and transform that data into actionable knowledge, in other words, it predicts what product may be interesting for a user and for the company.
Grupo AIA applies advanced algorithms in ML for developing RS to meet several targets:
Detecting users with similar behavior or interests
Obtaining users scoring of available products´ importance.
Give content recommendation to users based on their preferences and similar profiles.
Give content recommendation of similar products the user had interest in aiming at experiencing new contents based on their preferences.
Enables the study social trends and preferences evolution.
Enables to interpret social relationships, translate it into relevant information for the preparation of recommendation systems.