From a "Cold" to a "Warm" Start in Recommender Systems

Human is surrounded by a tremendous and scary amount of information on the web. That highlights the continuous need of recommendation systems in the different domains. Unfortunately cold start problem is still an important issue in these systems on new users and new items. The problem becomes more critical in systems that contain resources that lives too shortly like offers on products which stays only for few days (short life resources-SLiR). In this work we highlight how iSoNTRE (the intelligent Social Network Transformer into Recommendation Engine) solves this problem by using users' information in online social networks to overcome the cold start problem on new users, as well as iSoNTRE uses conceptual similarity, this overcomes the problem on new items, and on short life resources also. The work has been evaluated on Twitter on real users and results show that iSoNTRE succeeded in recommending offers to users with 14% of open rate on recommended offers, which is high compared to general open rate in social media, especially when we have nothing about users or offers before.