Trust based Personalized Recommender System

Abstract

We  rely  on  the  information from  our  trustworthy  acquaintances  to  help  us  take  even  trivial decisions  in  our  lives.  Recommender  Systems  use  the  opinions  of  members  of  a community  to  help individuals in that community identify the information most likely to be interesting to them or relevant to their needs. These systems use the similarity between the user and recommenders or between the items to form recommendation list for the user. They do not take into consideration the social trust network between the entities in the society to ensure that the user can trust the recommendations received from the system. The  paper proposes  a model  where a trust  network  exists  between the  peer  agents  and  the  personalized recommendations  are  generated on  the  basis  of  these  trust relationships.  The recommenders  personalize recommendations  by  suggesting only  those  movies  to user  that  matches  its  taste.  Also,  the social recommendation process is inherently fuzzy and uncertain. In the society, the information spreads through word-of-mouth and it is not possible to fully trust this information. There is uncertainty in the validity of such information. Again, when a product is recommended, it is suggested with linguistic quantifiers such as very  good,  more  or  less  good,  ordinary,  and so on.  Thus,  uncertainty  and  fuzziness  is  inherent in  the recommendation process. We have used Intuitionistic Fuzzy Sets to model such uncertainty and fuzziness in the recommendation process.