A Graph Model for Hybrid Recommender System

Recommender systems are the capable systems of providing essential information and removing unessential information for Internet users. The recommender systems are built based on two main information filtering techniques: Collaborative filtering and content-based filtering. Each method exploits particular aspects related to content features or product usage habit of users in the past to predict a brief list of the most suitable items with each user. In this paper, we propose a new unify method between collaborative filtering recommendation and content-based filtering recommendation based on graph model. The model allows us to shift hybrid filtering recommender problem to collaborative filtering recommender problem, then build new similarity measures based on graph to determine similarities between two users or two items, these similar measures are used to predict suitable items for users in the system. The experimental results on real data sets show that the proposed methods achieve superior performance compared to baseline methods.