Trust based Personalized Recommender System

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.