Alleviating cold-user start problem with users’ social network data in recommendation systems


The Internet and the Web 2.0 have radically changed the way of purchasing items, provoking the fall of geographic selling barriers all over the world. So large is the amount of data and items we can find in the Web that it turned out to be almost unmanageable. Due to this situation many algorithms have emerged trying to filter items for e-commerce users based in their tastes. In order to do this, these systems need information about the tastes of the users as input. This limitation is reduced as the users interaction with these systems increases. The main problem arises when new users enter a recommendation platform for the first time. The so called cold-start problem causes unsatisfactory random recommendations, which goes against these systems’ purpose. Cold-start includes users entering new systems, items, and even new systems. This situation challenges for new ways of obtaining user data. Social networks can be seen as huge information databases sources, and social network analysis would help us to do it using different techniques. In this paper, we present a solution which uses social network user data to generate first recommendations, alleviating the cold-user limitation. Besides, we have demonstrate that it is possible to reduce the cold-user problem applying our solution in a recommendation system environment.