Efficient Recommender System based on Graph Data for Multimedia Application

With the dawning ubiquitous computing age, increasing online-based multimedia data presents new challenges for storing and querying large amounts of data to online recommendation systems. Recent studies on recommendation systems show that graph data model is more efficient than relational data model for processing complex data. This paper proposes a new graph data storage model for the collaborative filtering-based recommendation system. Our proposed storage model efficiently filters out vertices which could not impact on calculating top-k recommended items in collaborative filtering algorithm. We present our structure, mechanisms and experimental results for improving the performance of recommender systems. For showing that proposed mechanisms are applicable in multimedia applications, we use real data set of the online site, MovieLense in the experiment. The result of the experiment shows that proposed approach is efficient storage model for recommendation system.