A generic graph-based multidimensional recommendation framework and its implementations

As the volume of information on the Web is explosively growing, recommender systems have become essential tools for helping users to find what they need or prefer. Most existing systems are two-dimensional in that they only exploit User and Item dimensions and perform a typical form of recommendation 'Recommending Item to User'. Yet, in many applications, the capabilities of dealing with multidimensional information and of adapting to various forms of recommendation requests are very important. In this paper, we take a graph-based approach to accomplishing such requirements in recommender systems and present a generic graph-based multidimensional recommendation framework. Based on the framework, we propose two homogeneous graph-based and one heterogeneous graph-based multidimensional recommendation methods. We expect our approach will be useful for increasing recommendation performance and enabling flexibility of recommender systems so that they can incorporate various user intentions into their recommendation process. We present our research result that we have reached and discuss remaining challenges and future work. Copyright is held by the International World Wide Web Conference Committee (IW3C2).