Using Social Networks to Design a Context Aware Recommender System in Mobile Commerce Platform

Nowadays, we are among a sheer volume of data and it is possible to make a wrong or non-optimal decision without a guide or appropriate pattern. Recommender systems have an efficient role in preventing information overload problem and helping user for making true and appropriate decision. Traditional collaborative filtering systems do not support context awareness in mobile commerce environment; furthermore, they lack high accuracy and require high computation volume. This paper proposes a new model for recommender system containing five steps that uses mobile data with user consent for identifying individuals with the highest influence for making recommendation and extracting current context of user, too. Then, the system uses the information of these impressive users in current context existed in social networks for making recommendations. At the evaluations section, it is showed that the proposed model obviates cold start problem in collaborative filtering systems and has a higher accuracy rather than them. In this step, different parameters using in methodology are changed and the impact of each is presented by diagrams.