Deriving temporal trends in user preferences through short message strings

Abstract

Short message strings are widely prevalent in the age of social networking. Taking Facebook as an example, a user may have many other users in his contact list. However, at any given time frame, the user interacts with only a small subset of these users. In this paper, we propose a recommender system that determines which users have common interests based on the content of the short message strings of different users. The system calculates the similarity between two users based on the contents of short message strings by the users over a certain time period. A similarity measure based on short message strings must be temporal study as the contents of the short messages vary rapidly over time. Experimental study is conducted in the Facebook domain using status updates of users. � 2016 IEEE.