Towards Personalized Offers by Means of Life Event Detection on Social Media and Entity Matching

In this paper we present a system for personalized offers based on two main components: a) a hybrid method, combining rules and machine learning, to find users that post life events on social media networks; and b) an entity matching algorithm to find out possible relation between the detected social media users and current clients. The main assumption is that, if one can detect the life events of these users, a personalized offer can be made to them even before they look for a product or service. This proposed solution was implemented on the IBM InfoSphere BigInsights platform to take advantage of the MapReduce programming framework for large scale capability, and was tested on a dataset containing 9 million posts from Twitter. In this set, 42K life event posts sent by 19K different users were detected, with an overall accuracy of 89% e precision of about 65% to detect life events. The entity matching of these 19K social media users against an internal database of 1.6M users returned 983 users, with accuracy of about 90%.