Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings

This paper introduces a new collaborative filtering recommender system that is capable of offering soft ratings as well as integrating with a social network containing all users. Offering soft ratings is known as a new methodology for modeling subjective, qualitative, and imperfect information about user preferences, as well as a more realistic and flexible means for users to express their preferences on products and services. Additionally, in the system, community preferences that are extracted from the social network are employed for overcoming sparsity and cold-start problems. In the experiment, the new system is tested using a data set culled from Flixster, a social network focused on movies. The experiment's results show that this system is more effective than the selected baseline in terms of recommendation accuracy.